Alireza Fatehi received the B.Sc. degree in electrical engineering from the Isfahan University of Technology, Isfahan, Iran, in 1990, the M.Sc. degree in electrical engineering from Tehran University, Tehran, Iran, in 1995, and the Ph.D. degree in electrical engineering from Tohoku University, Sendai, Japan, in 2001. From 2013 to 2015, he was a Visiting Professor with the Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada. He is currently an Associate Professor of electrical engineering with K.N. Toosi University of Technology (KNTU), Tehran. He is the Director of Advance Process Automation and Control research group and a Member of Industrial Control Center of Excellence with KNTU. His current research interests include industrial control systems, process control systems, intelligent systems, multiple model controller, nonlinear predictive controller, nonlinear identification, fault detection, soft sensor, and autonomous driving systems.
Brief Bio
Doctor of Philosophy, Electrical Engineering-Control Systems, March 2001
Tohoku University, Sendai, Japan
Thesis:” Multiple Modeling of Time-varying Systems by Self-organizing Map Neural Network“,
Supervisor: Professor Kenichi Abe
Master of Science, Electrical Engineering-Control Systems, January 1995
University of Tehran, Tehran, Iran
Thesis:” Design and Regulation of Intelligent Controllers for Industrial Processes”,
Supervisor: Professor Caro Lucas
(Second grade in Control Eng., Third in Electrical Eng., 1995-96)
Bachelor of Science, Electrical Engineering -Electronics, September 1990
Isfahan University of Technology, Isfahan, Iran.
Thesis:” Design construction of a low power Boost DC/DC converter“,
Supervisor:
Research
- Process Control Systems
- Condition Monitoring
- Performance Monitoring
- Fault Detection
- Soft Sensor
- Control Theories
- Multiple Model Controller
- Nonlinear Predictive Controller
- Non-monotonic Lyapunov Function
- Intelligent Systems
- Intelligent Controller
- Autonomous Driving Systems
Publication
Abstract: | Title | Year | Type |
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Abstract: This paper is concerned with robust identification of processes with time-varying time delays. In reality, the delay values do not simply change randomly, but there is a correlation between consecutive delays. In this paper, the correlation of time delay is modeled by the transition probability of a Markov chain. Furthermore, the measured data are often contaminated by outliers, and therefore, t-distribution is adopted to model the measurement noise. The variational Bayesian (VB) approach is applied to estimate the model parameters along with time delays. Compared with the classical expectation-maximization algorithm, VB approach has the advantage of capturing the uncertainty of the estimated parameter and time delays by providing their full probabilities. The effectiveness of the proposed method is demonstrated by both a numerical example and a pilot-scale hybrid-tank experiment. | Robust estimation of ARX models with time varying time delays using variational Bayesian approach Yujia Zhao, Alireza Fatehi, Biao Huang IEEE transactions on cybernetics | 2018 | Journal |
Abstract: In this paper, we consider an important practical industrial process identification problem where the time delay can change at every sampling instant. We model the time-varying discrete time-delay mechanism by a Markov chain model and estimate the Markov chain parameters along with the time-delay sequence simultaneously. Besides time-varying delay, processes with both time-invariant and time-variant model parameters are also considered. The former is solved by an expectation-maximization (EM) algorithm, while the latter is solved by a recursive version of the EM algorithm. The advantages of the proposed identification methods are demonstrated by numerical simulation examples and an evaluation on pilot-scale experiments. | A data-driven hybrid ARX and markov chain modeling approach to process identification with time-varying time delays Yujia Zhao, Alireza Fatehi, Biao Huang IEEE Transactions on Industrial Electronics | 2017 | Journal |
Abstract: State estimation for a system with irregular rate and delayed measurements is studied using fusion Kalman filter. Lab data in process plants is usually more accurate compared to other measurements. However, it is often slow rate and subject to variable delay and irregularity in sampling time. Fast rate state estimation can be conducted using fast rate measurement, while the slow rate lab data can be used to improve the accuracy of estimation whenever it is available. For this purpose, two Kalman filters are used to estimate the states based on each type of measurement. The estimates are fused in the next step by considering the correlation between them. An iterative algorithm to obtain the cross-covariance matrix between the estimation errors of the two Kalman filters is presented and employed in the fusion process. The improvement on the accuracy of estimation and comparison with other optimal fusion state estimation techniques are discussed through a simulation example, a pilot-scale experiment and an industrial case study. | Kalman filtering approach to multi-rate information fusion in the presence of irregular sampling rate and variable measurement delay Alireza Fatehi, Biao Huang Journal of Process Control | 2017 | Journal |
Abstract: This paper proposes a model bank selection method for a large class of nonlinear systems with wide operating ranges. In particular, nonlinearity measure and H-gap metric are used to provide an effective algorithm to design a model bank for the system. Then, the proposed model bank is accompanied with model predictive controllers to design a high performance advanced process controller. The advantage of this method is the reduction of excessive switch between models and also decrement of the computational complexity in the controller bank that can lead to performance improvement of the control system. The effectiveness of the method is verified by simulations as well as experimental studies on a pH neutralization laboratory apparatus which confirms the efficiency of the proposed algorithm. | Multi-linear model set design based on the nonlinearity measure and H-gap metric Davood Shaghaghi, Alireza Fatehi, Ali Khaki-Sedigh ISA transactions | 2017 | Journal |
Abstract: The topic of control performance assessment techniques has drawn lots of attentions and many performance assessment indices have been proposed. These indices are focused on certain malfunctions. Fallacious decision would result in if it is based on individual indices. Therefore, fusion of different indices can improve the assessment accuracy. In this paper, Bayesian and Dempster-Shafer theory are used individually to establish decision fusion strategy and tackle the performance assessment challenge of heavy duty gas turbine. To study the uncertainty effect on these methods, they are applied to the well-known Rowen model of gas turbine. The results illustrate the effectiveness of the proposed performance assessment method of the gas turbine model on the one hand, and the superiority of the Bayesian method when the uncertainty is low and that of Dempster-Shafer theory in the presence of uncertainty, on the other hand. | Performance monitoring of heavy duty gas turbines based on Bayesian and Dempster-Shafer theory Siyamak Afshar Khamseh, Alireza Fatehi Electrical and Information Technologies (ICEIT), 2017 International Conference on | 2017 | Conference |
Abstract: State estimation and fusion is studied using Kalman filter (KF) when a slow-rate integrated measurement is available. Integrated measurement is common in industrial processes, when a sample of material is gradually collected over some period of time and then sent to a laboratory for analysis. In this case, the laboratory measurement will reflect the material properties that have been integrated over the sampling period. The goal is to estimate the fast-rate value of states that evolve with time. A modified KF is proposed to execute state estimation using a slow-rate integrated measurement. Fusion of the slow-rate state estimate and other fast-rate measurements can improve the final state estimation of the process. The performance of the proposed method is demonstrated through both simulation and experimental study in a laboratory scale hybrid tank pilot plant. | State Estimation and Fusion in the Presence of Integrated Measurement Alireza Fatehi, Biao Huang IEEE Transactions on Instrumentation and Measurement | 2017 | Journal |
Abstract: The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information. | Attention control learning in the decision space using state estimation Zahra Gharaee, Alireza Fatehi, Maryam S Mirian, Majid Nili Ahmadabadi International Journal of Systems Science | 2016 | Journal |
Abstract: Control performance assessment techniques are widely studied and many performance assessment indices have been proposed. In this paper, a control performance assessment technique for multi-loop control systems is presented based on the decision fusion strategy. Since decisions based on individual indices can lead to erroneous results, decision fusion of different indices can improve the assessment accuracy, especially in multi- loop control systems in the presence of loop interactions. Performance assessment indices are individually evaluated and decisions based on these indices are fused. The results of simulation and practical implementation on series cascade control structures illustrate the effectiveness of the proposed algorithm. | Control performance assessment based on sensor fusion techniques S Afshar Khamseh, A Khaki Sedigh, B Moshiri, A Fatehi Control Engineering Practice | 2016 | Journal |
Abstract: In this contribution, full probability distribution of parameters of ARX model is obtained for on-line problems by means of Bayesian approach and Markov chain Monte Carlo method (MCMC), which provides the ability to be applied on time-varying ARX models as well. Full probability distribution of parameters represent whole available knowledge of parameters. So, decision makers can follow any policies to make decision about point estimation, like dynamic point estimation. Moreover, the Bayesian approach has great potential in combining sources of knowledge much more easier. To decrease the computational efforts, full probability of model parameters are updated based on size-varying partitions. Furthermore, incorporating the posterior probability of previous partition into the jump probability of current partition, in MCMC method, improves the performance of the proposed algorithm from the computation and convergence rate point of view. Simulation results demonstrate the effectiveness and validity of the proposed algorithm. | On-line Full Probability Distribution Identification of ARX Model Parameters Based on Bayesian Approach Amir HoseinValadkhani, Aminollah Khormali, Mahdi Aliyari Shoorehdeli Hamid Khaloozadeh Alireza Fatehi IFAC-PapersOnLine | 2016 | Journal |
Abstract: This paper presents a simple analytical method for tuning the parameters of fractional order PI (FOPI) controllers based on Bode's ideal transfer function. The proposed technique is applicable to stable plants describable by a fractional order counterpart of first order transfer function without time delay. Tuning rules are given in order to improve the robustness of the compensated system in the presence of gain uncertainty in the plant model. Finally, the designed FOPI controller is implemented on a laboratory scale twin rotor helicopter and comparison results are provided to show the effectiveness of the proposed tuning rules. | Robust Fractional Order PI Controller Tuning Based on Bode’s Ideal Transfer Function Roohallah Azarmi, Mahsan Tavakoli-Kakhki, Ali Khaki Sedigh, Alireza Fatehi IFAC-PapersOnLine | 2016 | Journal |
Abstract: Gaussian process (GP) regression is a fully probabilistic method for performing non-linear regression. In a Bayesian framework, regression models can be made robust by using heavy-tailed distributions instead of using normal distribution for modeling noise. This work focuses on estimation of parameters for robust GP regression. In literature, these are learned by maximizing the approximate marginal likelihood of data. However, gradient-based optimization algorithms which are used for this purpose can be unstable or may require tuning. In this work, an EM algorithm based approach is derived and implemented to infer the parameters. The pros and cons of the two approaches are analyzed. The advantage of EM algorithm lies in its ease of implementation and theoretical guarantees of numerical stability and convergence while its prediction performance is still comparable to gradient-based approaches. In some cases EM algorithm may be slow to converge. To circumvent this issue a faster EM based approach known as Expectation Conjugate Gradient (ECG) is implemented on robust GP regression. Finally, the proposed EM approach to robust GP regression is validated using an industrial data set. | Robust Gaussian process modeling using EM algorithm Rishik Ranjan, Biao Huang, Alireza Fatehi Journal of Process Control | 2016 | Journal |
Abstract: In this paper, an analytical method for tuning the parameters of the set-point weighted fractional order PID (SWFOPID) controller is proposed. The studied control scheme is the filtered fractional set-point weighted (FFSW) structure. Also to achieve a desired closed-loop performance, a fractional order pre-filter is employed. The proposed method is applicable to stable plants describable by a simple three-parameter fractional order model. Such a model can be considered as the fractional order counterpart of a first order transfer function without time delay. Finally, the proposed method is implemented on a laboratory scale CE 150 helicopter platform and the results are compared with those of applying a filtered fractional order PI (FFOPI) controller in a similar structure. The practical results show the effectiveness of the proposed method. | Analytical design of fractional order PID controllers based on the fractional set-point weighted structure: Case study in twin rotor helicopter Roohallah Azarmi, Mahsan Tavakoli-Kakhki, Ali Khaki Sedigh, Alireza Fatehi Mechatronics | 2015 | Journal |
Abstract: The main point of this paper is to present an iterative optimization strategy for tuning the parameters of Smith predictor based fractional order PID (SPFOPID) controller. The control scheme considered in this paper is the standard Smith predictor structure. Also, the internal model is considered to be a First Order Plus Dead Time (FOPDT) transfer function. Finally, the proposed method is implemented on a multi input-multi output (MIMO) flow-level plant and the obtained results are compared with the results of applying Smith predictor based PID controller (SPPID) in the similar structure. | Design and implementation of Smith predictor based fractional order PID controller on MIMO flow-level plant Roohallah Azarmi, Ali Khaki Sedigh, Mahsan Tavakoli-Kakhki, Alireza Fatehi Electrical Engineering (ICEE), 2015 23rd Iranian Conference on | 2015 | Conference |
Abstract: A non-monotonic Lyapunov function (NMLF) is deployed to design a robust H2 fuzzy observer-based control problem for discrete-time nonlinear systems in the presence of parametric uncertainties. The uncertain nonlinear system is presented as a Takagi and Sugeno (T–S) fuzzy model with norm-bounded uncertainties. The states of the fuzzy system are estimated by a fuzzy observer and the control design is established based on a parallel distributed compensation scheme. In order to derive a sufficient condition to establish the global asymptotic stability of the proposed closed-loop fuzzy system, an NMLF is adopted and an upper bound on the quadratic cost function is provided. The existence of a robust H2 fuzzy observer-based controller is expressed as a sufficient condition in the form of linear matrix inequalities (LMIs) and a sub-optimal fuzzy observer-based controller in the sense of cost bound minimization is obtained by utilising the aforementioned LMI optimisation techniques. Finally, the effectiveness of the proposed scheme is shown through an example. | Non-monotonic robust H2 fuzzy observer-based control for discrete time nonlinear systems with parametric uncertainties Siavash Fakhimi Derakhshan, Alireza Fatehi International Journal of Systems Science | 2015 | Journal |
Abstract: A new approach for modeling and monitoring of the multivariate processes in presence of faulty and missing observations is introduced. It is assumed that operating modes of the process can transit to each other following a Markov chain model. Transition probabilities of the Markov chain are time varying as a function of the scheduling variable. Therefore, the transition probabilities will be able to vary adaptively according to different operating modes. In order to handle the problem of missing observations and unknown operating regimes, the expectation maximization algorithm is used to estimate the parameters. The proposed method is tested on two simulations and one industrial case studies. The industrial case study is the abnormal operating condition diagnosis in the primary separation vessel of oil-sand processes. In comparison to the conventional methods, the proposed method shows superior performance in detection of different operating conditions of the process. | Operating condition diagnosis based on HMM with adaptive transition probabilities in presence of missing observations Nima Sammaknejad, Biao Huang, Weili Xiong, Alireza Fatehi, Fangwei Xu, Aris Espejo AIChE Journal | 2015 | Journal |
Abstract: An important feature in dynamic systems that model behavior in human society is the role of expectations formed by individual agents within such systems. Unlike physical system in engineering, social systems are inhabited by sentient decision makers that react to their environment and form expectations about future events and the decisions of other agents in the society. As a consequence, conventional tools used in engineering may not apply. However, a large body of contributions in the engineering literature to the field of intelligent systems may still be useful for the analysis of expectational dynamic social systems. This paper adapts an emerging literature on agentbased computational economics, exemplified in (LeBaron and Tesfatsion (2008); Oeffner (2008); Tesfatsion (2002 )), to the issue of deriving from-the-ground-up formulas of expectations useful for macro-economic analysis. It joins established optimization methods in economics with results in multi-agent predictive control from the engineering literature summarized in (Cao, Yu, Ren and Chen (2013); Maestre, Muñoz de la Peña and Camacho (2011 )). The benefit to this approach is that the resulting dynamic structures under individual optimization are causal and easily solved, unlike typical non-causal rational expectations models. | A multiagent predictive approach for modeling expectations formation Moeen Mostafavi, Alireza Fatehi, Hamed Shakouri, Peter von zur Muehlen | 2014 | Conference |
Abstract: This paper presents a neutral system approach to the design of an H∞ controller for input delay systems in presence of uncertain time-invariant delay. It is shown that when proportional derivative (PD) controller is applied to a time-delay system, the resulting closed loop system is generally a time-delay system of neutral type with delay term coefficients depending on the controller parameters. A descriptor model transformation is used to derive an advantageous bounded real lemma representation for the system. Furthermore, new delay-dependent sufficient conditions for the existence of an H∞ PD and PI controller in presence of uncertain delay are derived in terms of matrix inequalities. Some case studies and numerical examples are given in order to illustrate the advantages of the proposed method. | A neutral system approach to H∞ PD/PI controller design of processes with uncertain input delay A Shariati, HD Taghirad, A Fatehi Journal of Process Control | 2014 | Journal |
Abstract: This paper presents a novel procedure for classification of normal and abnormal operating conditions of a process when multiple noisy observation sequences are available. Continuous time signals are converted to discrete observations using the method of triangular representation. Since there is a large difference in the means and variances of the durations and magnitudes of the triangles at different operating modes, adaptive fuzzy membership functions are applied for discretization. The expectation maximization (EM) algorithm is used to obtain parameters of the different modes for the durations and magnitudes assuming that states transit to each other according to a Markov chain model. Applying Hamilton's filter, probability of each state given new duration and magnitude is calculated to weight the membership functions of each mode previously obtained from a fuzzy C-means clustering. After adaptive discretization step, having discrete observations available, the combinatorial method for training hidden Markov models (HMMs) with multiple observations is used for overall classification of the process. Application of the method is studied on both simulation and industrial case studies. The industrial case study is the detection of normal and abnormal process conditions in the primary separation vessel (PSV) of an oil sand industry. The method shows an overall good performance in detecting normal and risky operating conditions. | Adaptive monitoring of the process operation based on symbolic episode representation and hidden Markov models with application toward an oil sand primary separation Nima Sammaknejad, Biao Huang, Alireza Fatehi, Yu Miao, Fangwei Xu, Aris Espejo Computers & Chemical Engineering | 2014 | Journal |
Abstract: In this paper, a simple method is presented for tuning weighted PIλ + Dμcontroller parameters based on the pole placement controller of pseudo-second-order fractional systems. One of the advantages of this controller is capability of reducing the disturbance effects and improving response to input, simultaneously. In the following sections, the performance of this controller is evaluated experimentally to control the vertical magnetic flux in Damavand tokamak. For this work, at first a fractional order model is identified using output-error technique in time domain. For various practical experiments, having desired time responses for magnetic flux in Damavand tokamak, is vital. To approach this, at first the desired closed loop reference models are obtained based on generalized characteristic ratio assignment method in fractional order systems. After that, for the identified model, a set-point weighting PIλ + Dμcontroller is designed and simulated. Finally, this controller is implemented on digital signal processor control system of the plant to fast/slow control of magnetic flux. The practical results show appropriate performance of this controller. | Design of set-point weighting PIλ+ Dμ controller for vertical magnetic flux controller in Damavand tokamak H Rasouli, A Fatehi Review of Scientific Instruments | 2014 | Journal |
Abstract: This paper presents a new approach for the stability analysis and controller synthesis of discrete-time Takagi-Sugeno fuzzy dynamic systems. In this paper, nonmonotonic Lyapunov function is utilized to relax the monotonic requirement of Lyapunov theorem which renders larger class of functions to provide stability. To this end, three new sufficient conditions are proposed to establish global asymptotic stability. In this regard, the Lyapunov function decreases every few steps; however, it can be increased locally. Moreover, a new method is proposed to design the state feedback controller. It is shown that the Lyapunov function and the state feedback control law can be obtained by solving a set of Linear Matrix Inequalities (LMI) or Iterative Linear Matrix Inequalities (ILMI) which are numerically feasible with commercially available softwares. Finally, the exhausted numerical examples manifest the effectiveness of our proposed approach and that it is less conservative compared with the available schemes. | Non-monotonic Lyapunov functions for stability analysis and stabilization of discrete time Takagi-Sugeno fuzzy systems Siavash Fakhimi Derakhshan, Alireza Fatehi Int. J. Innov. Comput. Inf. Control | 2014 | Journal |
Abstract: In this paper, based on the nonmonotonic Lyapunov functions, a new less conservative state feedback controller synthesis method is proposed for a class of discrete time nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy systems. Parallel distributed compensation (PDC) state feedback is employed as the controller structure. Also, a T-S fuzzy observer is designed in a manner similar to state feedback controller design. The observer and the controller can be obtained separately and then combined together to form an output feedback controller by means of the Separation theorem. Both observer and controller are obtained via solving a sequence of linear matrix inequalities. Nonmonotonic Lyapunov method allows the design of controllers for the aforementioned systems where other methods fail. Illustrative examples are presented which show how the proposed method outperforms other methods such as common quadratic, piecewise or non quadratic Lyapunov functions. | Nonmonotonic observer-based fuzzy controller designs for discrete time TS fuzzy systems via LMI Siavash Fakhimi Derakhshan, Alireza Fatehi, Mehrad Ghasem Sharabiany IEEE transactions on cybernetics | 2014 | Journal |
Abstract: In this paper, an adaptive multiple model predictive controller (AMMPC) based on multiple model switching and tuning strategy and dynamic matrix control (DMC) system is presented to construct switching-tuning adaptive multiple model predictive controller (STAMMPC). Disadvantages of non adaptive multiple model predictive control (MMPC) in regulation and disturbance rejection are discussed and new robust adaptive supervisors to improve the decision making procedures are developed. Experimental results on pH neutralization process show that the proposed decentralized control strategy using STAMMPC algorithm has desirable performance and robustness characteristics and is superior to the other MMPC algorithms, especially in the case of the participation with suggested new adaptive disturbance rejection supervisor. | Switching-Tuning Adaptive Multiple Model Predictive Control Ali Shamsaddinlou, Alireza Fatehi, Ali Khaki Sedigh Journal of Control Engineering and Technology | 2014 | Journal |
Abstract: A multiple model structure of a prototype industrial gas turbine system is constructed under normal operation using a systematic method that incorporates non-linearity measure and H-gap metric tools with the multiple models technique. First, two new non-linearity indices for multiple input–multiple output systems are introduced and employed for decomposing the operating space of a gas turbine into some linear and non-linear modes. The non-linear modes may be further partitioned into some linear modes. The input and output data in each of the linear modes are used to construct an initial multiple model structure. In order to avoid the increase of the number of linear local models, the H-gap metric is extended to multiple input–multiple output systems and used to measure the similarity between linear local models and to merge the similar models. As a result, an algorithm is proposed for construction of multiple linear local models. The algorithm is employed for the identification of a single-shaft prototype industrial gas turbine. | Automatic model bank selection in multiple model identification of gas turbine dynamics SeyedM Hosseini, Alireza Fatehi, Ali K Sedigh, Tor A Johansen Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering | 2013 | Journal |
Abstract: In this paper, Fault Detection and Isolation (FDI) is studied for the rotary kiln of Saveh White Cement Company. To do so, K-means algorithm as a crisp clustering, Fuzzy C-Means (FCM), and Gustafson-Kessel (GK) algorithms as fuzzy clustering are used. In those, for finding number of clusters, Cluster Validity Indices (CVI) are applied. Principal Component Analysis (PCA) mapped the clusters into two dimensional spaces. Fault detection and isolation performance are evaluated by three criteria namely sensitivity, specificity, and confusion matrix. The results reveal that GK fuzzy algorithm provides better performance on detection and isolation of fault in this industrial plant. | Fault Detection and Isolation of a cement rotary kiln using fuzzy clustering algorithm Nayereh Talebnezhad, Alireza Fatehi, Mahdi Aliyari Shoorehdeli Fuzzy Systems (IFSC), 2013 13th Iranian Conference on | 2013 | Conference |
Abstract: Control of pH neutralization process has always been one of challenging problem in process control. The method presented here to control this process is the fuzzy identification of systems using Wiener model, and then multiplying the measured signal by the inverse of the nonlinear part of model. Therefore, we can design a linear controller for this new augmented system. This strategy is implemented in a generalized predictive control. One of the advantages of this control structure is consideration of explicit constraint in control of systems which also included in proposed fuzzy predictive control. At the end, the proposed method is tested on the model of a pH neutralization process. | Generalized predictive control of pH neutralization process based on fuzzy inverse model Davood Shaghaghi, Hossein MonirVaghefi, Alireza Fatehi Fuzzy Systems (IFSC), 2013 13th Iranian Conference on | 2013 | Conference |
Abstract: One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part of the process, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and non-linear dynamic equations. These equations have not completely extracted yet. Even in special cases, however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we present non-linear predictor and simulator models for a real cement rotary kiln by using non-linear identification technique on the locally linear neuro-fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise 15-minute horizon prediction for a cement rotary kiln are presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. At the end, the characteristics of these models are expressed. Furthermore, we present the pros and cons of these models. The data collected from White Saveh Cement Company is used for modelling. | Identification of non-linear predictor and simulator models of a cement rotary kiln by locally linear neuro-fuzzy technique Masoud Sadeghian, Alireza Fatehi International Journal of Mechatronics and Automation 2 | 2013 | Journal |
Abstract: This brief presents a X–Y pedestal using the feedback error learning (FEL) controller with adaptive neural network for low earth orbit (LEO) satellite tracking applications. The aim of the FEL is to derive the inverse dynamic model of the X–Y pedestal. In this brief, the kinematics of X–Y pedestal is obtained. To minimize or eliminate the backlash between gears, an antibacklash gearing system with dual-drive technique is used. The X–Y pedestal is implemented and the experimental results are obtained. They verify the obtained kinematics of the X–Y pedestal, its ability to minimize backlash, and the reduction of the tracking error for LEO satellite tracking in the typical NOAA19 weather satellite. Finally, the experimental results are plotted. | Implementation and Control of X–Y Pedestal Using Dual-Drive Technique and Feedback Error Learning for LEO Satellite Tracking Taheri, A.,M.Aliyari Sh., H. Bahrami, M.H. Fatehi Control Systems Technology, IEEE Transactions on | 2013 | Journal |
Abstract: This paper studies identification of a process in both frequent and infrequent operating points to design a nonlinear model predictive controller. Although, many of industrial processes normally work around an operating point, however they should seldom work in some infrequent points as well. In this case, due to low ratio of data points, identification of the processes based on all data set results in poor identification of the infrequent operating points. To resolve this problem, in this paper, at the first step, a data clustering strategy is used to group the data in different operating points. Since the ratio of infrequent to frequent data points is extremely low, the strategy used is the fuzzy Gath-Geva clustering methodology. Then, at the second step, a new approach has been proposed to compromise performance of identification of the nonlinear model for frequent and infrequent operating points. It is shown that if the ratio of data associated with frequent operating point to data of infrequent operating point is appropriately selected, the performance of the model remains satisfactory in the frequent operating point while the performance in the infrequent operating point is significantly improved as well. The proposed method gives an interval for appropriate ratio of data set in the highly nonlinear pH neutralization process. | Nonlinear System Identification in Frequent and Infrequent Operating Points for Nonlinear Model Predictive Control Alireza Fatehi, Behnaneh Sadeghpour, Batool Labibi Information Technology and Control | 2013 | Journal |
Abstract: This paper concerns application of data-derived approaches for analyzing and monitoring chemical process instruments, extracting product information, and designing estimation models for primary process variables, or difficult to measure in real-time variables. Modeling of process with an optimized classical neural network, the multi-layer perceptron (MLP) is discussed. Tennessee Eastman Process, a well-known plant wide process benchmark, is applied to validate the proposed approach. Investigations and several algorithms as step response test, Lipschitz number method and forward selection are used. The main advancement introduced here is that a hierarchical level responsible strategy is applied for selection of input variables and respective efficient time delays to attain the highest possible prediction accuracy of the neural network model for industrial process identification. | Optimized real-time soft analyzer for chemical process using artificial intelligence Mohammad Mahdi Karimi, Alireza Fatehi, Reza Ebrahimpour, Ali Shamsaddinlou Control Conference (ASCC), 2013 9th Asian | 2013 | Conference |
Abstract: Nonlinear behavior and disturbance sensitivity of the pH processes causes them to be known as an appropriate test bench for advanced controllers. Because of special behavior and varying parameters of pH processes, Multiple Model Predictive Controllers (MMPC) outperform other controllers from both regulation and disturbance rejection points of views. Two new supervisory methods based on prediction error and fuzzy weighting for MMPC are presented. Better regulation in special condition and most excellent disturbance rejection in comparison to other MMPC methods are achieved. | Study of Multiple Model Predictive Control on a pH neutralization plant Ali Shamsaddinlou, Alireza Fatehi, Ali Khaki Sedigh, Mohammad Mahdi Karimi Control Conference (ASCC), 2013 9th Asian | 2013 | Conference |
Abstract: In this paper input-output pairing is done based on concept of energy. Parseval theorem and cross-covariance samples of input-output are used for estimation of energy. After approximating interaction energy between input and output of the plant, input-output pairing is fulfilled. Through examples, it is illustrated that proposed method is appropriate for input-output pairing. The result is compared with Effective Relative Energy Array (EREA) as another energy based approach for input-output pairing. | An Improved Input-output Pairing Method based on Concept of Energy A Ahmadi-Tabatabaei, A Fatehi, A Khaki-Sedigh Advanced Materials Research | 2012 | Conference |
Abstract: A helicopter has nonlinear dynamics and it'sa multivariable system. A helicopter is an unstable plant with high level of interaction between some of its variables. Therefore, controlling a helicopter is very difficult and to control it we must use special strategies. A fuzzy controller is a kind of nonlinear controller. It can control a plant without a mathematical model or a poor model. In this paper, we designed a fuzzy controller for an unmanned helicopter with finite degree of freedom. The fuzzy controller is designed based on optimal controller strategies. The proposed method has a better performance than state feedback optimal controller. | Design of an Optimal Fuzzy Controller for Hover mode of Finite Degree of Freedom Unmanned Helicopter Sina Ameli, Alireza Fatehi, Mohamad Ataei Journal of Intelligent Procedures in Electrical Technology | 2012 | Journal |
Abstract: In this paper Fault Detection and Isolation (FDI) is shown as a pattern classification problem which can be solved using clustering techniques. Gath-Geva clustering (GGC) is exploited as optimal form by a performance assessment rule for fault detection, while multistage Gath-Geva clustering is employed for the intent of fault isolation. Furthermore since Visbreaker unit is a large scale process, a novel hybrid method on the basis of Principle Component Analysis and Genetic Algorithm optimization was also proposed in order to cope with the curse of dimensionality and complexity of computation problems. There are two main percentile criteria for validation of fault detection namely specificity and sensitivity. Evaluation of fault isolation has been depicted in confusion matrix. For analysis and visualization of the correlated high dimensional data, PCA maps the data point into lower dimensional space. The proposed FDI approaches have been evaluated through experimental Visbreaker process unit data collected in oil refinery. | Fault detection and isolation of Visbreaker unit in oil refinery using multistage Gath-Geva clustering Mohammad Mokhtare, Somayeh Hekmati Vahed, Mahdi Aliyari Shoorehdeli, Alireza Fatehi Electrical Engineering (ICEE), 2012 20th Iranian Conference on | 2012 | Conference |
Abstract: A new switching mechanism for multiple model adaptive controllers (MMAC) is suggested in this paper. The proposed method gives superior performance in comparison to the widely used method of switching based on performance function and hysteresis function in systems which experience high levels of measurement noise. This method acts as a complementary condition within the switching mechanism which checks the existence of excitation in system at every instant. The new method is evaluated by simulation studies on a nonlinear model of a pH neutralization plant. | Improved switching for multiple model adaptive controller in noisy environment Pouya Bashivan, Alireza Fatehi Journal of Process Control | 2012 | Journal |
Abstract: A combined approach for bumpless transfer multiple model predictive control (Multiple MPC) is proposed based on the Lyapunov function. State-space representation is used to design the controllers and the Lyapunov approach is employed to ensure closed loop stability. The proposed method uses both an intermediate controller and a bumpless mechanism in a unified configuration based on the stability analysis. Previous works on the bumpless multiple MPC design do not ensure closed loop stability, while the mechanism presented in this paper ensures both closed loop stability and applicable control performance for industrial processes. Finally, efficiency of the proposed method is validated by simulation results on non-isothermal continuous stirred-tank reactor (CSTR) system. | Lyapunov based multiple model predictive control: An LMI approach Mohammad Abdollahpouri, Ali Khaki-Sedigh, Alireza Fatehi American Control Conference (ACC), 2012 | 2012 | Conference |
Abstract: This paper provides a systematic method for model bank selection in multi-linear model analysis for nonlinear systems by presenting a new algorithm which incorporates a nonlinearity measure and a modified gap based metric. This algorithm is developed for off-line use, but can be implemented for on-line usage. Initially, the nonlinearity measure analysis based on the higher order statistic (HOS) and the linear cross correlation methods are used for decomposing the total operating space into several regions with linear models. The resulting linear models are then used to construct the primary model bank. In order to avoid unnecessary linear local models in the primary model bank, a gap based metric is introduced and applied in order to merge similar linear local models. In order to illustrate the usefulness of the proposed algorithm, two simulation examples are presented: a pH neutralization plant and a continuous stirred tank reactor (CSTR). | Multiple model bank selection based on nonlinearity measure and H-gap metric SeyedMehrdad Hosseini, Alireza Fatehi, Tor Arne Johansen, Ali Khaki Sedigh Journal of Process Control | 2012 | Journal |
Abstract: In this paper a new approach is proposed to design state feedback controllers for well known TS fuzzy systems. The used controller structure is in familiar parallel distributed compensation (PDC) format and the Lyapunov candidate is the frequently used common quadratic one. The only difference is the stability theorem used which is a revised version of Lyapunov stability theorem that relaxes monotonic condition. It is shown that the proposed method is less conservative than common quadratic method and piecewise method. | Non-monotonic fuzzy state feedback controller design for discrete time TS fuzzy systems Siavash Fakhimi Derakhshan, Alireza Fatehi, Mehrad Ghasem Sharabiany Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on | 2012 | Conference |
Abstract: … | On the structural optimization of a neural network model predictive controller Mahsa Sadeghassadi, Alireza Fatehi, Ali Khaki Sedigh, SeyedMehrdad Hosseini Industrial & Engineering Chemistry Research | 2012 | Journal |
Abstract: Input-Output data modeling using multi layer perceptron networks (MLP) for a laboratory helicopter is presented in this paper. The behavior of the two degree-of-freedom platform exemplifies a high order unstable, nonlinear system with significant cross-coupling between pitch and yaw directional motions. This paper develops a practical algorithm for identifying nonlinear autoregressive model with exogenous inputs (NARX) and nonlinear output error model (NOE) through closed loop identification. In order to collect input-output identifier pairs, a cascade state feedback (CSF) controller is introduced to stabilize the helicopter and after that the procedure of system identification is proposed. The estimated models can be utilized for nonlinear flight simulation and control and fault detection studies. | Two-degree-of-freedom helicopter closed-loop identification through a cascade controller Pouya Ghalei, Alireza Fatehi, Mohamadreza Arvan Advanced Materials Research | 2012 | Journal |
Abstract: Measuring the distance between two linear time invariant systems (LTIs) and its application are investigated by a very common and useful method (v-gap metric) and in following some related problems are raised. In many times we are interested in comparing two linear systems with their different frequency ranges and it is essence to have a metric which could cover all over frequency ranges of two plants. So by keeping the same topology of v-gap metric, a new metric is defined to measure distance between two linear systems. This metric is the extension of the v-gap metric on the linear systems from H∞ norm to H2 norm space. It is shown that all relations and equations which are used in v-gap can be proof in new space. The new metric not only has the ability of standard v-gap metric for measuring distance but also has some advantages. In addition by some simulations these two metrics are compared. | A new metric for measuring the distance between two linear systems SeyedMehrdad Hosseini, Alireza Fatehi, Ali Khakisedigh Control, Automation and Systems (ICCAS), 2011 11th International Conference on | 2011 | Conference |
Abstract: Expectation formation plays a principal role in economic systems. We examine and revise the standard rational expectations (RE) model, generally taken as the best paradigm for expectations modelling, and suggest a new method to model rational expectations. Conventional conditions that assert the stability and uniqueness of popular solution methods are shown to be insufficient. The agent-based new modelling approach suggested in this paper will be shown to lead to uniquely stable solutions. | A predictive multi-agent approach to model systems with linear rational expectations Moeen Mostafavi, Ali-Reza Fatehi, G Shakouri, Peter Von zur Muehlen Munich Personal RePEc Archive | 2011 | Journal |
Abstract: In this paper a neural-fuzzy controller is used to control cement kiln. The fuzzy controller is in the TSK form. The controller is trained during the control action due to cope with the plant changes. The most important aspects of this controller are first using couple of smaller controllers instead of a complete centralized one and second using the same framework that kiln operators use. ie the input variables that the controller use are the same input variables that the kiln operators use to control the same controlled variables. Joint together, decentralized fuzzy controller instead of a centralized fuzzy one has fewer parameters which need less memory and processing power of the controller. The proposed controller is tested on a simulator model which made on the real data of Saveh cement factory. The simulation results show the efficiency of the proposed controller. | An adaptive neuro fuzzy controller for cement kiln Mehrad G Sharabiany, Alireza Fatehi, Babak N Araabi Instrumentation Control and Automation (ICA), 2011 2nd International Conference on | 2011 | Conference |
Abstract: Relative gain array (RGA) is the most important and popular method of finding the best pairing in MIMO plants. However, selection of the pairs based on RGA is an offline algorithm with some ambiguity. In this article, the normalised RGA (NRGA) matrix is introduced through the combination of the RGA matrix and pairing rules. The pairing problem can be interpreted as an assignment problem by using NRGA. Therefore, Hungarian algorithm can be applied to pair inputs and outputs of the process. Up to this stage, integrity has not yet been considered. If the determined optimal pairing does not satisfy integrity conditions based on the Niederlinski index, the procedure continues through the suboptimal pairings. The algorithm is fully systematic. It is applied for control structure selection of the well known Tennessee Eastman process plant. | Automatic pairing of large scale MIMO plants using normalised RGA Alireza Fatehi International Journal of Modelling, Identification and Control | 2011 | Journal |
Abstract: The main purpose of this paper is a study of the efficiency of different nonlinearity detection methods based on time-series data from a dynamic process as a part of system identification. A very useful concept in measuring the nonlinearity is the definition of a suitable index to measure any deviation from linearity. To analyze the properties of such an index, the observed time series is assumed to be the output of Volterra series driven by a Gaussian input. After reviewing these methods, some modifications and new indices are proposed, and a benchmark simulation study is made. Correlation analysis, harmonic analysis and higher order spectrum analysis are selected methods to be investigated in our simulations. Each method has been validated with its own advantages and disadvantages. | Comparison of nonlinearity measures based on time series analysis for nonlinearity detection Seyed Mehrdad Hosseini, Tor Arne Johansen, Alireza Fatehi Modeling, Identification and Control | 2011 | Journal |
Abstract: In this paper, we introduce a vector which is able to describe the Niederlinski Index (NI), Relative Gain array (RGA), and the characteristic equation of the relative error matrix. The spectral radius and the structured singular value of the relative error matrix are investigated. The cases where the perfect result of the Relative Gain Array, equal to the identity matrix, coincides with the least interaction in a plant are pointed out. Then, the Jury Algorithm is adopted to get some insight into interaction analysis of multivariable plants. In particular, for interaction analysis of 3×3 plants, simple yet promising conditions in terms of the Relative Gain Array and the NiederLinski Index are derived. Several examples are also discussed to illustrate the main points. | Descriptive vector, relative error matrix, and interaction analysis of multivariable plants Nima Monshizadeh-Naini, Alireza Fatehi, Ali Kahki-Sedigh Automatica | 2011 | Journal |
Abstract: In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input– output model is identified for the plant. To identify the various operation points in the kiln, locally linear neuro-fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 min prediction horizon. The other two models are presented for the two faulty situations in the kiln with 7 min prediction horizon. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used in this study. | Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique Masoud Sadeghian, Alireza Fatehi Journal of Process Control | 2011 | Journal |
Abstract: A 3-DOF image stabilizer (periscope) is modeled and controlled, such that target is fixed in the center of camera image. Its nonlinear dynamic equations are extracted by applying the Euler-Lagrange equation of rigid body motion in presence of friction. The extracted equations are second order motion equations. Moreover, fuzzy sliding mode controller (FSMC) for target tracking is applied to this nonlinear system where it is subject to uncertainty and external disturbances. The controller is MIMO and it has knowledge-based structure. This method eliminates chattering that exists in the conventional sliding mode. The effectiveness of the developed algorithm is validated by simulation results. | Modeling and fuzzy control of 3-DOF image stabilizer in presence of uncertainty and disturbance Elham Javanfar, AliReza Fatehi Control System, Computing and Engineering (ICCSCE), 2011 IEEE International Conference on | 2011 | Conference |
Abstract: This paper presents a Neuro-fuzzy based method using local linear model trees (LOLIMOT) train algorithm for nonlinear identification of a catalytic reformer unit in oil refinery plant. This unit include highly nonlinear behaviour and it is complicated to obtain an accurate physical model. There for, it is necessary to use such appropriate method providing suitable while preventing computational complexities. LOLIMOT algorithm as an incremental learning algorithm has been used several time as a well-known method for nonlinear system identification and estimation. For comparison, Multi Layer Perceptron (MLP) and Radial Bases Function (RBF) neural networks as well-known methods for nonlinear system identification and estimation are used to evaluate the performance of LOLIMOT. The results presented in this paper clearly demonstrate that the LOLIMOT is superior to other methods in identification of nonlinear system such as catalytic reformer unit (CRU). | Modeling and identification of catalytic reformer unit using locally linear model trees Mohammad Mokhtare, Somayeh Hekmati Vahed, Mahdi Aliyari Shoorehdeli, Alireza Fatehi Electrical Engineering (ICEE), 2011 19th Iranian Conference on | 2011 | Conference |
Abstract: Extreme nonlinearity and exhibition of severe interaction effects of multivariable pH processes makes it an appropriate test bed for evaluation of advanced controllers. This paper studies different multiple model methods for Generalized Predictive Control using Independent Model approach (GPCI) with adaptive weighting matrices. New method for adaptive determination of weighting matrices, proposed in this paper. Simulation results via typical multivariable pH process demonstrate the effectiveness and validity of the method. Different multiple model methods using adaptive weighting matrices compared with each other. | Multiple model predictive control of multivariable ph process using adaptive weighting matrices Peyman Bagheri, Vahid Mardanlou, Alireza Fatehi IFAC Proceedings Volumes | 2011 | Journal |
Abstract: Neural networks are known as powerful tools to represent the essential properties of nonlinear processes because of their global approximation property. However, a key problem in modeling nonlinear processes by neural networks is the determination of neuron numbers. In this paper, a data based strategy for determining number of hidden layer neurons based on the Barrons work, describing function analysis and bicoherence nonlinearity measure is proposed. The proposed algorithm is evaluated for a pH neutralization process. It is shown that this algorithm has acceptable results. | Optimal number of neurons for a two layer neural network model of a process Mahsa Sadegh Asadi, Alireza Fatehi, Mehrdad Hosseini, Ali Khaki Sedigh SICE Annual Conference (SICE), 2011 Proceedings of | 2011 | Conference |
Abstract: In this paper, a new approach for input-output pairing for stable and linear time invariant multivariable systems based on inputs-outputs correlation is introduced. Being independent from system's model is the characteristic of the proposed method. It is demonstrated that both static and dynamic properties of the system regarded in the proposed method. Through examples, the accuracy of the proposed approach is investigated. Finally, an example is used to show that in some cases Effective Relative Gain Array (ERGA) leads to improper pairs while the proposed method achieves the appropriate pairs. | The correlation based method for input-output pairing Adel Ahmadi Tabatabaei, Alireza Fatehi, Ali Khaki Sedigh SICE Annual Conference (SICE), 2011 Proceedings of | 2011 | Conference |
Abstract: Time delays are common in industrial processes. The information about the delay value of any process is valuable for both identification and control procedures. Several methods have been suggested for time delay estimation (TDE) in the literature. We propose a simple method based on plant input-output data. The concept of this data driven method is from combination of two well known approaches: Time delay estimation from impulse response and subspace identification. This method can be easily implemented in Multi Input-Multi Output (MIMO) plants. Also, by analyzing the window of input-output data in an online fashion, we can utilize our proposed method for time varying delay case. To verify the effectiveness of our proposed method, the developed procedures are applied to a pH plant model, a MIMO system and a time delay varying scenario. Simulation results demonstrate the effectiveness of the proposed method. | A subspace based method for time delay estimation Jafar Shalchian, Ali Khaki-Sedigh, Alireza Fatehi Communications, Control and Signal Processing (ISCCSP), 2010 4th International Symposium on | 2010 | Conference |
Abstract: In this paper, the relation between Input-output pairing and minimum variance (MV) index as a performance index is studied. Control structure selection or the input-output pairing problem is a key step in designing decentralized controllers for multivariable. The Relative Gain Array (RGA) is an important tool for the control structure selection procedure. In this study, RGA is calculated and decentralized minimum variance controllers are designed for each feasible pairing. The MV performance index will be calculated from the closed loop transfer function using the markov parameters. It is shown that the value of the MV index can propose an input-output pairing that leads to minimum output variance. Several simulation results are provided to show the main points of the paper. | Input-output pairing based on the control performance assessment index S Choobkar, A Khaki Sedigh, A Fatehi Advanced Computer Control (ICACC), 2010 2nd International Conference on | 2010 | Conference |
Abstract: Abstract The multiple modeling and controlapproach is a proper method for modeling and controlof nonlinear systems which their dynamics changesrapidly at different operating conditions. The dynamicof the twin rotor helicopter in vertical direction isnonlinear and changes instantaneously with respect tochange of the elevation angle, and it can be used forimplementation of multiple modeling and controlapproach. In this paper the performance of multiplemodeling and control approach by simulation andimplementation has been investigated. | Investigation of the Performance of MultipleModeling and Control Approach Using aLaboratory Helicopter V Nazarzehi, A Fatehi, M Zamanian International Journal of Computer and Electrical Engineering | 2010 | Journal |
Abstract: A nonlinear model predictive control (NMPC) algorithm based on a neural network model is proposed for multivariable nonlinear systems. A multi-input multi-output model is developed using multilayer perceptron (MLP) neural network which is trained by Levenberg-Marquardt algorithm and amplitude modulated pseudo random binary (APRBS) signals with noise as data sets. Model predictive control also uses Levenberg-Marquardt algorithm for the control signal optimization. The control performance is improved by using a disturbance model that compensates both model mismatch and external disturbance. The learning rate of disturbance estimation network changes adaptively to treat the model mismatch differently from the external disturbance. Simulation results using the quadruple-tank are employed to show the effectiveness of the method. | Neural network model-based predictive control for multivariable nonlinear systems Bahareh Vatankhah Alamdari, Alireza Fatehi, Ali Khaki-Sedigh Control Applications (CCA), 2010 IEEE International Conference on | 2010 | Conference |
Abstract: This paper disputes what Blanchard and Kahn have reported as the solution of linear rational expectation (RE) systems many years ago. Their method leads to traditional determinacy condition which is used very much nowadays. In this paper we have a new look to the mathematical procedure of this solution method and the main problem in their solution will be shown. We introduce a new methodology for modeling the systems with expectation, while in future this way of modeling can be used to replace traditional RE models. | Why the determinacy condition is a weak criterion in rational expectations models Moeen Mostafavi, G Shakouri, Ali-Reza Fatehi Munich Personal RePEc Archive | 2010 | Journal |
Abstract: In this paper we present a systematic procedure to design robust fuzzy controller for exponentially stabilizing affine nonlinear systems, based on their TS fuzzy model. For robust design we consider modeling error in TS model and as well as perturbation in the original nonlinear system. Minimization of cost function along with mapping closed loop poles to desired poles are considered simultaneously in controller design. As a result, the desired specified performance in transient response can be achieved. Piecewise Discontinues Lyapunov Functions (PDLF) are utilized in our proposed method. To avoid difficulties in boundary conditions in PDLF we opt to design an online controller and check the regions and boundaries continuously. The constraints required to guarantee the exponential stability of the original nonlinear systems and optimal controller design with guaranteeing desired performance are presented in the LMI form. The y well developed. The power of these methods is that searching Lyapunov function and feedback gain can be stated as a convex optimization problem and the task of finding the common Lyapunov function can be readily be formulated into an LMI problem. However this approach is too conservative and there are lots of stable systems that we can not find a common positive definite Lyapunov function for all subsystems. Piecewise quadratic Lyapunov function approach [7],[8] have been considered to avoid conservativeness of quadratic Lyapunov function approaches [4]-[6]. Piecewise quadratic Lyapunov function (PLF) are divided in two categories, one is continuous (PCLF) in boundaries and one of them is discontinuous (PDLF) on boundaries. It was shown that PDLF in contrast with PCLF results in fewer LMIs [9]. To apply all mentioned methods, the system must be presented by a Takagi-Sugeno model and as it was demonstrated TS modeling enables us to deal with high order complicated nonlinear systems. Most of works so far have used PCLF for controller design and stability analysis, but PDLF have been used mainly for stability analysis and there are no reports about using PDLF for controller design. The main reason is difficulties in boundary conditions. effectiveness and applicability of the proposed method is examined on an inverted pendulum system. | A new method to guarantee performance and robustness in optimal fuzzy controller design for perturbed nonlinear systems based on Piecewise Discontinues Lyapunov Function Masumeh Esfandiari, Babak N Araabi, Alireza Fatehi, Ahmadreza Vali Control and Automation, 2009. ICCA 2009. IEEE International Conference on | 2009 | Conference |
Abstract: In this paper, we use system identification methods for abnormal condition detection in a cement rotary kiln. After selecting proper inputs and output, an input–output model is identified for the plant’s normal conditions. A novel approach is used in order to estimate the delays of the input channels of the kiln before identification part. This method eases the identification since with determining the input channels delays, the dimension of search space in the identification part reduces. Afterward, to identify the kiln’s model, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOcally LInear MOdel Tree (LOLIMOT) algorithm which is an incremental tree-structure algorithm. Finally, with the model for normal condition of the kiln, the incident of abnormalities in output are detected based on the length of duration and magnitude of difference between the real output and model’s output. We distinguished three abnormal conditions in the kiln, two of which are known as common abnormal conditions and another one which was not characteristically known for cement experts either. | Abnormal condition detection in a cement rotary kiln with system identification methods Iman Makaremi, Alireza Fatehi, Babak Nadjar Araabi, Morteza Azizi, Ahmad Cheloeian Journal of process control | 2009 | Journal |
Abstract: Rotary cement kiln is the main part of a cement plant that clinker is produced in it. Clinker is the main ingredient of cement. Continual and prolonged operation of rotary cement kiln is vital in cement factories. However, prolonged operation of the kiln is not possible and periodic repairs of the refractory lining would become necessary, due to non-linear phenomena existing in the kiln, such as sudden falls of coatings in the burning zone and probability of damages to the refractory materials during production. This is the basic reason behind the needs for a comprehensive model which is severely necessary for better control of this process. Such a model can be derived based on the mathematic analysis with consultation of expert operator experiences. In this paper both linear and nonlinear model are identified for rotary kiln of Saveh white cement factory. The linear model is introduced using Box-Jenkins structure. The results of the obtained model were satisfactory compared to some other linear models and can be used for designing adaptive or robust controllers. Also, nonlinear system identification via Neural Network technique is performed and its result was compared to linear models. | Comparison of rotary cement kiln identified models G Noshirvani, A Fatehi, B Araabi, M Shirvani, M Azizi Control and Automation, 2009. ICCA 2009. IEEE International Conference on | 2009 | Conference |
Abstract: A multiple-model adaptive controller is developed using the Self-Organizing Map (SOM) neural network. The considered controller which we name it as Multiple Controller via SOM (MCSOM) is evaluated on the pH neutralization plant. In MCSOM multiple models are identified using an SOM to cluster the model space. An improved switching algorithm based on excitation level of plant has also been suggested for systems with noisy environments. Identification of pH plant using SOM is discussed and performance of the multiple-model controller is compared to the Self Tuning Regulator (STR). | Design of multiple model controller using SOM neural network P Bashivan, AR FATEHI JOURNAL OF CONTROL | 2009 | Journal |
Abstract: The CE 150 made by Humusoft is a laboratory helicopter designed for studying system dynamics and control engineering principles. This helicopter is nonlinear and unstable in vertical direction. In this paper a linear bank of models for modeling a vertical direction of helicopter has been obtained, and model validation is discussed. | Identification of linear models for a laboratory Helicopter Valimohammad Nazarzehi, Alireza Fatehi Computer and Electrical Engineering, 2009. ICCEE'09. Second International Conference on | 2009 | Conference |
Abstract: One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using nonlinear identification technique on the Locally Linear Neuro-Fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise fifteen minute prediction horizon for a cement rotary kiln is presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these models. The data collected from White Saveh Cement Company is used for modeling. | Identification of nonlinear predictor and simulator models of a cement rotary kiln by locally linear neuro-fuzzy technique Masoud Sadeghian, Alireza Fatehi Computer and Electrical Engineering, 2009. ICCEE'09. Second International Conference on | 2009 | Conference |
Abstract: In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study. | Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique Masoud Sadeghian, Alireza Fatehi Computer and Electrical Engineering, 2009. ICCEE'09. Second International Conference on | 2009 | Conference |
Abstract: This paper provides an extended pairing criterion based on the effective relative gain array. The extension is achieved in two steps. First, an energy based compromise between steady state gain and bandwidth information of the plant is proposed. Then, it is argued that the best pairing may depend on the closed-loop specifications. Thus, to make this extension practical and precise, a simple solution to take into account the bandwidth of the desired closed-loop plant is introduced. To show the effectiveness of the proposed method, several examples are discussed. These examples include the cases where the conventional ERGA leads to an appropriate result and is in agreement with the proposed pairing criterion. They also include the cases where the original ERGA leads to an improper pairing while the proposed method achieves the acceptable pairs. | Input− output pairing using effective relative energy array N Monshizadeh-Naini, A Fatehi, A Khaki-Sedigh Industrial & Engineering Chemistry Research | 2009 | Journal |
Abstract: Rotary cement kiln is the main part of a cement plant that clinker is produced in it. Continual and prolonged operation of rotary cement kiln is vital in cement factories. However, continual operation of the kiln is not possible and periodic repairs of the refractory lining would become necessary, due to non-linear phenomena existing in the kiln, such as sudden falls of coatings in the burning zone and probability of damages to the refractory materials during production. This is the basic reasoning behind the needs for a comprehensive model which is severely necessary for better control of this process. Such a model can be derived based on the mathematical analysis with consultation of expert operator experiences. In this paper linear model is identified for rotary kiln of Saveh white cement factory. The linear model is introduced using Box-Jenkins structure. The results of the obtained model were satisfactory compared to some other models and can be used for designing adaptive or robust controllers. | Linear Identification of Rotary White Cement Kiln. Golamreza Noshirvani, Mansour Shirvani, Alireza Fatehi ICINCO-SPSMC | 2009 | Conference |
Abstract: The purpose of this paper is to formulate truth-value assignment to self-referential sentences via Zadeh's truth qualification principle and to present new methods to assign truth-values to them. Therefore, based on the truth qualification process, a new interpretation of possibilities and truth-values is suggested by means of type-2 fuzzy sets and then, the qualification process is modified such that it results in type-2 fuzzy sets. Finally, an idea of a comprehensive theory of type-2 fuzzy possibility is proposed. This approach may be unified with Zadeh's Generalized Theory of Uncertainty (GTU) in the future. | Self-referential reasoning in the light of extended truth qualification principle Mohammad Reza Rajati, Hamid Khaloozadeh, Alireza Fatehi Intelligent Engineering Systems and Computational Cybernetics | 2009 | Journal |
Abstract: In this paper, two types of multiple-model adaptive controllers are practically evaluated on a laboratory-scale pH neutralization process. The first one is supervisory switching multiple-model adaptive controller (SMMAC) whose model bank is fixed and selected a priori, and another one is a controller based on multiple models, switching, and tuning strategy (MMST) which uses the possibility of model bank tuning. In addition to investigation of the effect of tuning, the advantage of a disturbance rejection supervisor is studied. Various experiments and exhaustive numerical analyses are provided to assess the abilities of the proposed algorithms. | The Effect of Tuning in Multiple-Model Adaptive Controllers: A Case Study Ehsan Peymani, Alireza Fatehi, Ali Khaki Sedigh IFAC Proceedings Volumes | 2009 | Journal |
Abstract: In this paper, a multiple models, switching, and tuning control algorithm based on poleplacement control is studied. Drawbacks of the algorithm in disturbance rejection are discussed, and a novel supervisor to enhance the decision-making procedure is developed. The modified algorithm is evaluated in a simulation study for a nonlinear pH neutralization process. Comparison results are provided to evaluate the performance and robustness characteristics of the proposed algorithm. | A disturbance rejection supervisor in multiple-model based control Ehsan Peymani, Alireza Fatehi, Ali Khaki Sedigh 8th International conference on control, Manchester, UK | 2008 | Conference |
Abstract: In this paper, we design a neurofuzzy controller to control several variables of a rotary cement kilns. The variables are back-end temperature, pre-heater temperature, oxygen content and CO2 gas content of the kiln. The fuzzy control system, as an advanced control option for the kilns, is intended to minimize the operator interaction in the control process. The proposed fuzzy controller uses a neural network to optimize TSK-type fuzzy controller. Since there is no generally applicable analytical model for cement kilns, we use the real data derived from Saveh cement factory for the plant identification. A model, which is very similar to the real plant, is identified then; and the identified model is used for control design and simulations. Extensive simulation studies justify the effectiveness and applicability of the proposed control scheme in intelligent control of cement plant. | A neuro-fuzzy controller for rotary cement kilns Maryam Fallahpour, Alireza Fatehi, Babak N Araabi, Morteza Azizi IFAC Proceedings Volumes | 2008 | Journal |
Abstract: Pairing is the first step of decentralized controller design procedure in multi-input multi- output (MIMO) processes. In spite of considerable efforts dedicated to this problem, most of the known pairing techniques are offline algorithms and fail to decide when dealing with high dimensional and/or time varying processes and adaptive control applications. In this article, normalized effective relative gain array (NERGA) is introduced as an effective automatic pairing method and is employed in a new adaptive decentralized PID control strategy. | A novel automatic method for multivariable process pairing and control Mohammad-Amin Moezzi, Alireza Fatehi India Conference, 2008. INDICON 2008. Annual IEEE | 2008 | Conference |
Abstract: Inherent nonlinearity of pH processes causes that they are recognized as an appropriate test bench for evaluation of advanced controllers. Because of special characteristics of them, it is evident that adaptive controllers outperform others. This paper presents a comparison between a conventional adaptive controller and a switching multiple-model adaptive one in both regulation and disturbance rejection points of view. A disturbance rejection supervisor is designed to improve the performance of the adaptive controllers in the presence of unmeasured disturbances. A laboratory scale pH process is used as an application example. | An experimental comparison of adaptive controllers on a pH neutralization pilot plant Ehsan Peymani, Alireza Fatehi, Pouya Bashivan, Ali Khaki Sedigh India Conference, 2008. INDICON 2008. Annual IEEE | 2008 | Conference |
Abstract: Tracking moving objects in variable cluttered environments is an active area of research. It is common to use some simplifying assumption in such environments to facilitate the design. In this paper a new method for simulating the completely non-Gaussian cluttered environments is presented. The method is based on using the variable variance of process noise as a description of variability in such environments. The key objective is to find an effective algorithm for tracking a single moving object in variable cluttered environments, with utilization of the presented method. The new methodology is presented in two steps. In the first step we compare the accuracy of estimators in tracking a moving object, and in the second step, the goal is to find the best algorithm for tracking a single moving target in variable cluttered environments. | An Improved Method for Tracking a Single Target in Variable Cluttered Environments Fatemeh Rahemi, Ali Khaki Sedigh, Alireza Fatehi, Farbod Razzazi Signal Processing and Information Technology, 2008. ISSPIT 2008. IEEE International Symposium on | 2008 | Conference |
Abstract: Control based on multiple models (MM) is an effective strategy to cope with structural and parametric uncertainty of systems with highly nonlinear dynamics. It relies on a set of local models describing different operating modes of the system. Therefore, the performance is strongly depends on the distribution of the models in the defined operating space. In this paper, the problem of on-line construction of local model set is considered. The necessary specifications of an autonomous learning method are stated, and a high-level supervisor is designed to add an appropriate model to the available model set. The proposed algorithm is evaluated in a simulated pH neutralization process which is a highly nonlinear plant and composed of both abrupt and large continuous changes. The preference of the multiple-model approach with learning ability on a conventional | Automatic learning in multiple model adaptive control Ehsan Peymani, Alireza Fatehi, A Khaki Sedigh International Conference on Control, UKACC, Manchester, UK | 2008 | Conference |
Abstract: Considering the need of an advanced process control in cement industry, this paper presents an adaptive model predictive algorithm to control a white cement rotary kiln. As any other burning process, the control scenario is to expect the controller to regulate the temperature and the period of baking a fixed quantity of raw material as desired, as well as to have the concentration of the combustion gases under control. To achieve these goals, this work presents a strategy which includes multivariable online identification of the kiln process and a constrained generalized predictive controller. An MLP neural network model derived from real plant data of Saveh cement factory in Iran is used as the kiln process simulator. The control efforts are made taken into account the operating constraints. At last the proposed control strategy is modified so as to gain good disturbance rejection ability. | Cement rotary kiln control: A supervised adaptive model predictive approach Javaneh Ziatabari, Alireza Fatehi, Mohamad TH Beheshti India Conference, 2008. INDICON 2008. Annual IEEE | 2008 | Conference |
Abstract: Using the assignment technique of operations research, it is possible to obtain optimal controllable and observable pairs based on gramian measure for decentralised control of MIMO plants. The advantages of using assignment method for both open and closed loop performance in a MIMO plant are discussed in this paper. | Decentralised control of MIMO plant using optimal gramian-based pairing Alireza Fatehi International Journal of Automation and Control | 2008 | Journal |
Abstract: In this paper a novel approach is proposed to solve a decentralized control problem to stabilize a multivariable system and attenuate the interconnections between its subsystems. To satisfy these conditions, an ut feedback controller is designed by solving an H∞ control problem. The designed controller is applied to a practical multivariable Flow-Level plant to show the effectiveness of the proposed methodology. The time delays, transmission zero, two different time constants, and the model uncertainties are the main problems in this plant. | Decentralized control of a multivariable flow-level plant based on robust control approach Yashar Kouhi, Batool Labibi, Alireza Fatehi, Rahman Adlgostar India Conference, 2008. INDICON 2008. Annual IEEE | 2008 | Conference |
Abstract: Neural Network Model Predictive Control (NN-MPC) combines reliable prediction of neural network with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. It is shown that this structure is prone to steady-state error when external disturbances enter or actual system varies from its model. In this paper, these model uncertainties are taken into account using a disturbance model with iterative learning which adaptively change the learning rate to treat gradual effect of the model mismatch differently from the drastic changes of external disturbance. Then, a high-pass filter on error signal is designed to distinguish disturbances from model mismatches. Practical implementation results as well as simulation results demonstrate good performance of the proposed control method. | Disturbance Rejection in Neural Network Model Predictive Control Alireza Fatehi, Houman Sadjadian, Ali Khaki-Sedigh, Ali Jazayeri IFAC Proceedings Volumes | 2008 | Journal |
Abstract: The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave. | Flexible structure multiple modeling using irregular self-organizing maps neural network Alireza Fatehi, Kenichi Abe International Journal of Neural Systems | 2008 | Journal |
Abstract: In this paper two robust controllers are designed for a practical Process Trainer Level plant. The system nonlinearity, time delay and change of parameters are the main problems in design of a desired controller for this plant. To design a controller, the linear models of the system and the disturbance models at different operating points are derived. Then, a parametric uncertainty profile is obtained by system identification strategies which is used in QFT control design. Indeed, for H∞ control design a multiplicative unstructured model is extracted from the parametric uncertainty. All constraints in control design, disturbance rejection and control signal are derived. Based on these constraints, appropriate controllers are determined. To improve robust performance μ-Synthesis with DK iteration is used. Finally all results are compared by applying the different controllers to the plant. | H∞ and QFT robust control designs for level control plant Y Kouhi, B Labibi, A Fatehi, R Adlgostar India Conference, 2008. INDICON 2008. Annual IEEE | 2008 | Conference |
Abstract: In this paper, we use system identification methods for abnormal condition detection of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. A novel approach is used in order to estimate the delays of the input channel of the kiln. By means of that, the identification task gets easier and the results are more accurate. To identify the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Finally, a model for the healthy mode of the kiln is obtained through which it is possible to detect abnormal conditions in the process. We distinguished two common abnormal conditions in kiln and another one which was not characteristically known for cement experts as well. | Identification and abnormal condition detection of a cement rotary kiln Iman Makaremi, Alireza Fatehi, Babak N Araabi, Morteza Azizi, Ahmad Cheloian IFAC Proceedings Volumes | 2008 | Journal |
Abstract: In this paper with reference to analytical results of different well-known relay feedback methods, we illustrate a main deficiency in parameter estimation of processes with a small ratio of time delay to time constant. Then to rectify this problem we introduce a modified relay feedback structure with additional delay to estimate the parameters of the FOPDT transfer function of the system. The significance of this method lies in the fact that many industrial plants perform fairly such as FOPDT systems, and a wide range of processes have negligible dead time versus their long constant time. Also, the estimated FOPDT transfer function from proposed relay feedback test can be used as a priori knowledge in advanced control strategies which need a FOPDT model of the system. The method is straightforward and simulation results illustrate the effectiveness, and simplicity of the proposed method. | Improved FOPDT model estimation with Delayed-relay feedback for constant time dominant processes Zeinab Tehrani Zamani, Behzad Moshiri, Ali Khaki Sedigh, Alireza Fatehi Prepr of UKACC Conf. on Control | 2008 | Conference |
Abstract: The paper deals with problem of estimating input channel delay in nonlinear system with a model-free approach. The proposed method is based on Lipschitz theory. It is an extension to the Lipschitz method which was proposed for determining the order of a model. Our algorithm consists of two parts which in the first one estimation is made on the proper number of dynamics on the input and in the second part the pure delay of the input is obtained. The method is applied for estimation of the delay of two different models and the estimation was as accurate as possible. | Lipschitz numbers: a medium for delay estimation Iman Makaremi, Alireza Fatehi, Babak Nadjar Araabi IFAC Proceedings Volumes | 2008 | Journal |
Abstract: A multiple-model adaptive controller is developed using the Self-Organizing Map (SOM) neural network. The considered controller which we name it as Multiple Controller via SOM (MCSOM) is evaluated on the pH neutralization plant. An improved switching algorithm based on excitation level of plant has also been suggested for systems with noisy environments. Identification of pH plant using SOM is discussed and performance of the multiple-model controller is compared to the Self Tuning Regulator (STR) controller. | Multiple-model control of pH neutralization plant using the SOM neural networks Pouya Bashivan, Alireza Fatehi, Ehsan Peymani India Conference, 2008. INDICON 2008. Annual IEEE | 2008 | Conference |
Abstract: In this paper a Neural Predictive Controller (NPC) designed to control a broad class of process systems. Neural network identification yields nonlinear global model of the unknown system. LevenbergMarquardt (L-M) optimization method is used to find optimal control signal to minimize future errors of the objective function of predictive controller. Inequality constraints of actuators are added to the objective function through a penalty term which increases drastically as it approaches the limitations. To use the controller for wide range of process systems, an initial phase runs before the main controller to determine parameters. This phase moves the system output to operating point and applies PID controller with APRBS reference signal. The gathered data are used to estimate parameters such as pure delay, prediction horizon, control coefficient and identification order. To validate the approaches, the controller has implemented in level, pressure and flow pilot plants and compared with conventional controller which shows faster and smoother tracking results. | Neural Predictive Control for Wide Range of Process Systems Ali Jazayeri, Alireza Fatehi, Houman Sadjadian, Ali Khaki-Sedigh proceedings control | 2008 | Conference |
Abstract: Closed loop identification of nonlinear model and control of a laboratory helicopter using genetic algorithm is proposed in this paper. The derived model has a nonlinear structure. Using the previous results of the physical modeling of the studied plant, a nonlinear model is considered based on the physical dynamics of the system. However, there is no need to perform numerous physical experiments to estimate the model parameters. Instead, genetic algorithm as a nonlinear optimization technique is used to obtain the parameters of the model. Therefore, the advantage of both modeling and identification methods are employed. In the next step, the parameters of a multi input-multi output (MIMO) PID controller for the derived model will be tuned by GA using the obtained nonlinear model as a simulator of the plant. Applying the controller to both the real plant and the simulation model, the accuracy of the model and the performance of the controller is examined. The results demonstrate that the achieved model accurately fits to the behavior of the real plant and the controller designed based on this model, can control the real system appropriately. | Nonlinear identification and MIMO control of a laboratory helicopter using genetic algorithm Hanif Tahersima, Alireza Fatehi India Conference, 2008. INDICON 2008. Annual IEEE | 2008 | Conference |
Abstract: Brain emotional learning based intelligent controller (BELBIC) is based on computational model of limbic system in the mammalian brain. In recent years, this model was applied in many linear and nonlinear control applications. Previous studies show that this controller has fast response, simple implementation and robustness with respect to disturbances. It is also possible to define emotional signal based on control application objectives. But in the previous studies, internal instability of this controller was not considered and control task were done in limited time period. In this article mathematical description of BELBIC is investigated and improved to avoid internal instability. Simulation and implementation of improved model was done on level plant. The obtained results showed that instability of model has been solved in the new model without loss of performance by using Integral Anti Windup (IAW). | Real-Time Level Plant Control Using Improved BELBIC Mojtaba Masoudinejad, Rahman Khorsandi, Alireza Fatehi, Caro Lucas, S Fakhimi Derakhshan, Mohammad Reza Jamali IFAC Proceedings Volumes | 2008 | Journal |
Abstract: Performance assessment and monitoring of control systems can be used to improve the performance of industrial processes. In this paper, a novel relay feedback based method for monitoring and automatic retuning of a class of proportional-integral (PI) controllers is proposed for the systems with gain nonlinearity. For performance assessment of the closed loop system, a time domain evaluation criteria based on the integral of the absolute value of the error (IAE) and the normalized pick of the error in setpoint (SP) changes are presented. Simulation results on the highly nonlinear pH process have shown the effectiveness and feasibility of this method. | Relay feedback based monitoring and autotuning of processes with gain nonlinearity Zeinab Tehrani Zamani, Behzad Moshiri, Alireza Fatehi, Ali Khaki Sedigh Proc. UK Automatic Control Conference, Manchester, UK | 2008 | Conference |
Abstract: This paper uses potential clustering approach to perform online fuzzy clustering. This method is an improvement of the subtractive clustering which is a noniterative clustering algorithm and so is suitable for online applications. In Spite of all capabilities of the potential clustering, this method suffers from a major disadvantage. The number of clusters grows fast when the sensitivity of the algorithm is increased. In this article an innovative technique has been proposed to reduce the number of clusters. The proposed method is applied to the Macky-Glass benchmark. It is shown although the number of clusters is reduced; the resulting performance will not be affected. | A new approach for online fuzzy identification by potential clustering including rule reduction Ali Karimoddini, K Salahshoor, A Fatehi, M Karimadini Control Conference (ECC), 2007 European | 2007 | Conference |
Abstract: Rotary Kiln is the central and the most complex components of cement production process. The first point at the beginning of the process, which is called back-end, is the calcining zone of the kiln and has a significant role on the quality of the clinker. In this paper to control the back-end temperature of a rotary kiln, we propose a fuzzy controller based on the operator's behaviors. We concentrate on how we can control the back-end temperature of a rotary kiln using a fuzzy controller. The work presented in this paper, is to use the advantages of fuzzy logic techniques to propose and develop a supervisory control system for the cement plant process having complex dynamics. The performance of the proposed controller is investigated by applying it to a simulator of the plant, derived from an MLP neural network model. Finally, the controller's behavior is evaluated in a disturbance environment. Simulation results show that the functionality of the controller causes a smooth behavior in both controller and model outputs. | A supervisory fuzzy control of back-end temperature of rotary cement kilns Maryam Fallahpour, Alireza Fatehi, Babak N Araabi, Morteza Azizi Control, Automation and Systems, 2007. ICCAS'07. International Conference on | 2007 | Conference |
Abstract: Balanced realization has the advantage of producing some valuable information on controllability and observability (C/O) of the plant. This specification was used in pairing of MIMO plants to some SISO subplants. Using balanced realization, the pairs with better C/O are selected. In this paper the problem of pairing based on balanced realization is interpreted as an assignment problem. Therefore the Hungarian algorithm can utilize to solve the pairing problem. The algorithm is fully systematic and may be utilize in online and adaptive pairing. The pairing algorithm is also developed to reject any undesired pair like uncontrollable and/or unobservable pairs. With some modification, it is also applied to nonsquare plants. | An algorithm for systematic pairing of square and nonsquare MIMO plants using balanced realization interaction measure AR FATEHI JOURNAL OF CONTROL | 2007 | Journal |
Abstract: Input/output pairing is an important task in control of a MIMO process by some SISO controllers. Relative gain array (RGA) is the most important method to find the best pairing. But selection of pairs based on RGA is an offline algorithm which needs some human decision. In this article, Normalized RGA (NRGA) matrix is introduced through the combination of the RGA matrix and its selection rules. Using NRGA, pairing problem can be interpreted as an assignment problem. The well known Hungarian algorithm is applied to the above problem to obtain the optimal pairing. | Automatic pairing of MIMO plants using normalized RGA A Fatehi, A Shariati Control & Automation, 2007. MED'07. Mediterranean Conference on | 2007 | Conference |
Abstract: A comprehensive step-by-step approach to the construction of a global fuzzy TSK model for a real SISO nonlinear system in closed loop is proposed. First the overall existing nonlinear distortions are evaluated, and then sub optimal experiments are designed so that the impacts of distortion are minimized. Such experiments can result in models which are consistent local estimations of the true underlying linear system at different operating points. Finally by suitable fuzzy combination of extracted open loop local models, a global fuzzy simulation model is constructed. Employing a quasi tailor-made parameterization, a model refinement can be carried out by trimming the membership functions through any optimization algorithms. In opposite of the conventional tailor-made parameterization algorithm, the stability of this modified algorithm is guaranteed. | Closed-loop global fuzzy tsk modeling: A case study Hamidreza Azimian, Alireza Fatehi, BN Araabi Control & Automation, 2007. MED'07. Mediterranean Conference on | 2007 | Conference |
Abstract: In this paper, two methods of designing controller for a practical MIMO Flow-Level pilot plant are achieved. Since the open loop plant is unstable, it is stabilized by using inner controller. Then the stabilized plant is identified at different operating points. A nominal model which is non-minimum phase is selected. This system has two outputs which have completely different time constants. Therefore, the identification problem is difficult. The other problem associated with the system is the time delay that is one of the characteristics of the process plants. The transfer function of system is close to triangular and the RGA matrix of plant is close to the identity matrix. Hence, decentralized control can be a good selection for this system which is used in our work. The robustness of the system with decentralized controller is also checked. Two Hinfin, robust controllers based on the knowledge of the disturbance are derived and the results are compared to the results of the decentralized one. To have a good robust performance the mu-synthesis and DK-iteration approach is used. | Multivariable control design for MIMO flow-level control plant Y Kouhi, R Adlgostar, B Labibi, A Fatehi, S Fakhimi EUROCON, 2007. The International Conference on" Computer as a Tool" | 2007 | Conference |
Abstract: In this paper, a nonlinear fuzzy identification approach based on genetic algorithm (GA) and Takagi-Sugeno (TS) fuzzy system is presented for fuzzy modeling of a multi-input, multi- output (MIMO) dynamical system. In this approach, GA is used for tuning the parameters of the membership functions of the antecedent parts of IF-THEN rules and Recursive Least- Squares (RLS) algorithm is employed for parameter estimation of the consequent linear sub- model parts of the TS fuzzy rules. The presented method is implemented on a simulated nonlinear MIMO distillation column. The results show that the presented method gives a more accurate model in comparison with the conventional TS fuzzy identification approach. | Multivariable GA-based identification of TS fuzzy models: MIMO distillation column model case study Borhan Molazem Sanandaji, Karim Salahshoor, Alireza Fatehi Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International | 2007 | Conference |
Abstract: In this paper, a method for designing a stable fuzzy controller for nonlinear systems with the ability of reference tracking is introduced. First, the nonlinear equations of model helicopter is presented which is modeled by Humusoft Inc. based on linearization around system operating points, a TS fuzzy model is represented. Then, based on state feedback a TS fuzzy controller is developed, which its stability is guaranteed by nonlinear matrix Inequalities (LMI). These inequalities are solved using convex optimization methods by means of Matlab and LMI toolbox. In the following, tracking problem, control signal constraints are discussed. | Stable Fuzzy Controller Design for a Nonlinear Helicopter Model Hanif Tahersima, Mahdi Kaveh, Alireza Fatehi Innovative Computing, Information and Control, 2007. ICICIC'07. Second International Conference on | 2007 | Conference |
Abstract: This paper presents a neuro-fuzzy based method using local linear model trees (LOLIMOT) train algorithm for nonlinear identification of a temperature control pilot plant. Such systems include highly nonlinear behavior and it is complicated to obtain an accurate physical model. Therefore, it is necessary to use such appropriate tools providing suitable models while preventing computational complexities. The identification results of pilot plant confirm the high performance of proposed method in two operational modes. | An experimental nonlinear system identification based on local linear neuro-fuzzy models Hamidreza Nourzadeh, Alireza Fatehi, Batool Labibi, Babak Nadjar Araabi Industrial Technology, 2006. ICIT 2006. IEEE International Conference on | 2006 | Conference |
Abstract: This paper discusses application of an intelligent system in order to navigate in real-time a small size, four wheeled, indoor mobile robot accurately using ultra-light (160 gr), inexpensive laser range finder without prior information of the environment. A recurrent neural network is used to find the best path to the target of the robot. An accurate grid-based map is generated using a laser range finder scene and location found by a modified dead reckoning system. Finally a motion control method is presented. These approaches are implemented and tested in Resquake mobile robot | Mobile robot navigation in an unknown environment A Jazayeri, A Fatehi, H Taghirad Advanced Motion Control, 2006. 9th IEEE International Workshop on | 2006 | Conference |
Abstract: In this paper, we will present population based method for placement of center of radial basis function of a locally linear neuro-fuzzy (LLNF) network, which is trained by LOLIMOT algorithm. Originally, LOLIMOT algorithm incrementally divides the hyper-rectangles on input space into two axes orthogonal directions in half. However, this heuristic method would not be the best possible partitioning of the space. We present and evaluate a new particle swarm optimization (PSO) method for finding the best divisions of input space in the LOLIMOT algorithm | Particle swarm extension to LOLIMOT Ramin Mehran, Alireza Fatehi, Caro Lucas, Babak Nadjar Araabi Intelligent Systems Design and Applications, 2006. ISDA'06. Sixth International Conference on | 2006 | Conference |
Abstract: In this paper, different approaches to solve the forward kinematics of a three DOF actuator redundant hydraulic parallel manipulator are presented. On the contrary to series manipulators, the forward kinematic map of parallel manipulators involves highly coupled nonlinear equations, which are almost impossible to solve analytically. The proposed methods are using neural networks identification with different structures to solve the problem. The accuracy of the results of each method is analyzed in detail and the advantages and the disadvantages of them in computing the forward kinematic map of the given mechanism is discussed in detail. It is concluded that ANFIS presents the best performance compared to MLP, RBF and PNN networks in this particular application. | Neural networks approaches for computing the forward kinematics of a redundant parallel manipulator H Sadjadian, HD Taghirad, A Fatehi International Journal of Computational Intelligence | 2005 | Journal |
Abstract: Pairing is an important task in controlling of a MIMO plant by some SISO sub- control systems. Using balance realization is one of the methods to find the best pairing. In this article, an algorithm is proposed to find an optimal controllable and observable input- output pairing. The algorithm can utilize when the plant parameters change and propose the new pairing to adapt the control structure. | Adaptive optimal controllable and observable pairing by the assignment method based on the balanced realization measure Alireza Fatehi Proc. of Control’04 Conference | 2004 | Conference |
Abstract: In this paper an H∞ controller is designed for a hydraulically actuated active suspension system of a half-modeled vehicle in a cascade feedback structure. Using the proposed structure the nonlinear behavior of actuator is reduced significantly. In the controller synthesis, a proportional controller is used in the inner loop, and a robust H∞ controller forms the outer loop. Two H∞ controllers are designed for this system. First unstructured uncertainty is not considered in the design procedure and secondly, the controller is designed considering uncertainty. Each of these controllers is designed in a decentralized fashion and the vehicle oscillation in the human sensitivity frequency range is reduced to a minimum. Statistical analysis of the simulation result using random input as road roughness, illustrates the effectiveness of the proposed control algorithm for both cases. | Decentralized Robust H∞ Controller Design for a Half–Car Active Suspension System A Shariati, HD Taghirad, A Fatehi Proceedings of Control 2004 | 2004 | Conference |
Abstract: Because of increasingly application of fuzzy systems, great deal of attention has been paid to design of fuzzy controller system. Many methods of fuzzy controller design have been suggested such as genetic algorithm and neural network. In this paper, our purpose is to design fuzzy controller rule base by GA. In fact, by GA through possible rule base we search for a subset of optimal rules for fuzzy control of ball and plate system. Design of this controller is based on linear form of ball and plate system with the aid of Simulink and finally the result is implemented on plant. | Optimal Design and Regulation of Fuzzy Controller with Genetic Algorithm for a Ball and Plate System. MM Takami, A Fatehi Iranian Conference on Electrical Engineering, ICEE 2003, Shiraz University, Shiraz, May 6-8, 2003 | 2003 | Conference |
Abstract: Multiple modeling identification using the selforganizing map neural network has been introduced by authors [1]. Two variations of that have been presented; MMSOM and MMISOM. MMSOM is based on using ordinary SOM and MMISOM utilizes the irregular SOM. In MMISOM, the neighborhood between the nodes may change. Therefore, MMISOM has more flexibility to cover concave spaces while SOM is more suitable for convex spaces. In this paper, after a review of both algorithms, some of the properties of MMISOM on the presence of noise are discussed. | Noise Effect on Multiple Modeling by SOM Neural Networks Alireza Fatehi, Kenichi Abe Iranian Conference on Electrical Engineering, ICEE 2003, Shiraz University, Shiraz, May 6-8, 2003 | 2002 | Conference |
Abstract: … | Multiple modeling of time-varying systems by self-organizing map neural network Alireza Fatehi … | 2001 | 0 |
Abstract: … | Self-organizing map neural network as a multiple model identifier for timevarying systems A Fatehi, K Abe Sixth International Symposium on Artificial Life and Robotics (AROB 6th’01) | 2001 | Conference |
Abstract: An algorithm to derive a multiple models set for a plant by the use of the self-organising map (SOM) were introduced by the authors (1999). The statistical properties of the models are investigated in this paper. As a plant, we consider a linear time invariant one. The parameters of the plant at each step are selected randomly with a specified distribution. Based on this distribution, the point distribution of the parameters of the multiple models is derived for this plant and compared with the plant parameters distribution. | Convergence of SOM multiple models identifier Alireza Fatehi, Kenichi Abe Systems, Man, and Cybernetics, 1999. IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on | 1999 | Conference |
Abstract: A method for Multiple identification based on the self-organizing map neural networks is presented and some of its properties is investigated. Inputs to the NN are instantaneous parameters and so the reference vectors of the networks outputs are the parameter estimation of the multiple models. | Plant identification by SOM neural networks Alireza Fatehi, Kenichi Abe Control Conference (ECC), 1999 European | 1999 | Conference |
Abstract: Fuzzy logic controllers (FLC) has shown good performances on the controlling of the plants. In spite of this fact, FLC is an ill-defined function for analyzing. Fortunately GA is a good optimizer tools for the ill-defined functions, down to just measurable ones. From 1989 that Karr et al introduced the first GA optimizer for FLCs, it attracted many researchers. This paper overlooks these efforts in the last 10 years. It is not possible to write down the complete list of references have been used in this abstract. So we just mentioned the earliest and some of the other main references. A complete list and report are available from. | Challenges on the design of the fuzzy logic controller by the genetic algorithms Alireza Fatehi, Kenichi Abe, Caro Lucas 2nd Int. workshop on intelligent systems of the 4th joint conf. on information systems | 1998 | Conference |