Conference Papers

According to the repeated-game theory, the continual interactions of electricity producers can increase the probability of collusion between these firms. As a result, the market equilibrium will be established based on tacit collusion. In this paper, the model of collusion in a power pool market is presented in the form of a mathematical program with equilibrium constraint (MPEC). This program takes into account the transmission network and production capacity constraints of the power plant, and the uncertainty property caused by the demand shock. The uncertainty is shown by a random variable in this scenario. Assuming that the probability density function of the random variable is general knowledge for all stakeholders of the electricity market, and the risk tolerance is zero, the collusion resulting from the strategy due to uncertainty is shown as the market equilibrium. Finally, it is concluded that the collusive expected profits in anticipation of a high demand market are higher than a low demand market.

In the recent years, artificial neural network have been used to improvement of system identification. The performance of neural network directly depends on the hidden layer, which include weights and activation functions of the network. In addition Genetic Algorithms are used to learn of neural network as a type of evolutionary computing algorithms. In this paper, the structure of hidden layers and weights are modified by using biological neuron model of Izhikevich. These two methods, Genetic Algorithms and biological model of neuron, merge together for designing a novel structure.

In This study, we present a new sensor fault detection approach based on nonlinear parity technique in presence of sensor noise. Conventionally analytical redundancy (AR) was used to fault detection and isolation in linear systems. The proposed parity space approach with nonlinear analytical redundancy (NLAR) technique can be applied to detect sensor fault in the nonlinear affine systems with mentioned class. The proposed approach will be implemented in pH neutralization system. At the end nonlinear fault detection and identification algorithm will be successfully implemented, examined and reported.

In this study, a novel fuzzy unknown input observer for robust fault estimation scheme is developed when both faults and unknown input are considered. The proposed scheme includes component fault with nonlinear distribution matrix in state equation, unknown input signal in state and output equations. After that, Takagi-Sugeno (T-S) model is used to create multiple models. While T-S model is used for only the nonlinear distribution matrix of the fault signal, a larger category of nonlinear system will be included. Two set of observers are considered, the first one is extended fuzzy unknown input observer (EFUIO) and the other one is fuzzy sliding mode observer (FSMO). The approach decoupled the faulty subsystem from the rest of the system through a series of linear transformations. Then, the objective is to design EFUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method. Unknown input is removed; meanwhile, FSMO is designed for faulty subsystem to guarantee estimation of fault. Sufficient conditions are established in order to guarantee the convergence of the state estimation error and the results are formulated in the form of linear matrix inequalities (LMIs). Finally, a simulation study on an electromagnetic suspension system (EMS) is presented to demonstrate the performance of the results compared with a pure SMO.

In dealing with model predictive controllers (MPC), controller tuning is a key design step. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a recently proposed analytical MPC tuning approach based on low order models. The performance of such methods deteriorates in dealing with unknown or time varying parameter plants. To overcome this problem, adaptive MPC tuning strategies are practical alternatives. The adaptive MPC tuning approach proposed in this paper is based on on-line identification and analytical tuning formulas. Simulation results are used to show the effectiveness of the proposed methodology.

Optimal solution for nonlinear identification problem in the presence of non-Gaussian distribution measurement and process noises is generally not analytically tractable. Particle filters, known as sequential Monte Carlo method (SMC), is a suboptimal solution of recursive Bayesian approach which can provide robust unbiased estimation of nonlinear non-Gaussian problem with desire precision. On the other hand, Hunt-Crossley is a widespread nonlinear model for modeling telesurgeries environment. Hence, in this paper, particle filter is proposed to capture most of the nonlinearities in telesergerie environment model. An online Bayesian framework with conventional Monte Carlo method is employed to filter and predict position and force signals of environment at slave side respectively to achieve transparent and stable bilateral teleoperation simultaneously. Simulation results illustrate effectiveness of the algorithm by comparing the estimation and tracking errors of sampling importance resampling (SIR) with extended Kalman filter.

In this paper, a novel architecture in multilayer perceptron (MLP) neural network with flexible activation function and adaptive learning rate is presented for a data-driven identification of robot dynamics. It is assumed that the measurement of robot end-effector position, velocity and acceleration are available corrupted by Gaussian noise. Since some general property of robot dynamics are included in the proposed structure as well as optimization indices, this structure is envisaged having good performance in confronting with uncertainty in measurements. The main contribution of this paper is to propose a transparent neural network structure for identification of dynamic terms by introducing a gray-box identifier. Simulation results on 2-DOF serial manipulator reveal the accuracy of the method. Finally, experimental results on a laboratory-scaled twin rotor CE 150 helicopter indicate the applicability of the proposed method.

Unmanned Aerial Vehicles (UAVs) pose a multi-input and multi-output (MIMO) dynamic structure, making their simultaneous guidance and control too complicated to be maintained via conventional scalar controllers. In this paper, a multivariable optimal controller is introduced based upon LQG\LTR design approach to effectively control the UAV attitude in the presence of noise and disturbance. The regulator design problem is solved by generating an optimal state estimate using a Kalman filter. A loop transfer recovery (LTR) procedure is developed to allow good recovery of the full state feedback properties, enhancing stability and performance robustness. This scheme facilitates proper integration of system's gain at different frequencies in order to provide optimal bandwidth and yet weakening the noise effects. The corresponding rate of return gains is set in frequency-domain to achieve robust performance characteristics. A set of tests is conducted on an UAV simulation case study to explore its performance under different scenarios. The results clearly demonstrate well performances in the face of the induced noise and couplings between the system channels.

This study presents fault detection of a heavy duty V94. 2 gas turbine which has 162.1 MW nominal power and 50 Hz nominal frequency and is located at Pareh Sar power plant, Gilan, Iran. For this purpose stored data include measurements of relative and absolute vibration of shaft bearings in both turbine and compressor sections. Signal processing techniques and mathematical transformations are used for feature extraction, as well as supervised and unsupervised methods for dimensionality reduction. Finally neural networks are employed for classification task and fault detection results for different methods are compared and discussed. Proposed techniques show zero FAR and MAR, when PNN is used with PCA or when MLP or RBF is used with LDA for dimensionality reduction.

Successful implementation of predictive controller requires an appropriate tuning of its parameters. Closed form tuning equations are practically rewarding as they can be easily implemented with relatively low computational costs. In this paper, a tuning strategy for the generalized predictive control of single input-single output and multi input-multi output plants is presented. First order plus dead time model of the plant is considered and analysis of variance and nonlinear fitting is employed to derive tuning equations. Finally, simulation results are used to verify the efficiency of the proposed tuning strategy.

Real time knowledge of total mass of gas and liquid in the annulus and geological properties of the reservoir is very useful in many active controllers, fault detection systems and safety applications in the well during petroleum exploration and production drilling. Sensors and instrumentation can not measure the total mass of gas and liquid in the well directly and they are computed by solving a series of nonlinear algebraic equations with measuring the choke pressure and the bottom-hole pressure. This paper presents different estimator algorithms for estimation of the annular mass of gas and liquid, and production constants of gas and liquid from the reservoir into the well during Under Balanced Drilling. The results show that all estimators are capable of identifying the production constants of gas and liquid from the reservoir into the well, while the Lyapunov based adaptive observer gives the best performance comparing with other methods when there is a significant amount of noise.

It is not possible to directly measure the total mass of gas and liquid in the annulus and geological properties of the reservoir during petroleum exploration and production drilling. Therefore, these parameters and states must be estimated by online estimators with proper measurements. This paper describes a nonlinear Moving Horizon Observer to estimate the annular mass of gas and liquid, and production constants of gas and liquid from the reservoir into the well during Under-Balanced Drilling with measuring the choke pressure and the bottom-hole pressure. This observer algorithm based on a low-order lumped model is evaluated against Joint Unscented Kalman filter for two different simulations with low and high measurement noise covariance. The results show that both algorithms are capable of identifying the production constants of gas and liquid from the reservoir into the well, while the nonlinear Moving Horizon Observer achieves better performance than the Unscented Kalman filter.

Modern systems are required to guarantee a high degree of safety and self-diagnostics capabilities. This paper investigates the problem of state fault diagnosis in nonlinear systems with modeling uncertainties. In contrast with common literature, the fault diagnosis scheme is proposed in discrete time domain. This property relaxes the risk of stability and performance degradation in deriving discrete equivalent of continuous methods. An estimator is designed in order to generate residual signal by utilizing a proper nonlinear state transformation. A robust compensator term is implemented in estimator to decrease effect of modeling uncertainties and approximation error on residual signal. When the residual signal is exceeded detection threshold, an on-line fault approximator is turned on and trained by appropriate parameter update law. An extra term is considered in update rule to overcome the need of persistency of excitation (PE). The implement of all robust compensator term, PE relaxing term and proper parameter adaption law improve the accuracy of fault reconstruction. The result would be obviously vital in tolerant and time-life prediction stages after fault diagnosis.

Yaw instability of automotive vehicles occurs dangerous accidents particularly while driving on wet or icy surfaces. Considering wet or icy situations as faults, fault tolerant controllers are suitable to handle the control of automotive vehicles. In order to have yaw stability and increasing maneuverability and safety of faulty systems, using control allocation methods are good choices. This paper proposes a control allocation method based on the pseudo inverse along the null space of the control matrix (PAN) to establish lateral stabilization in automotive vehicle.

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.

In this study, fault tolerant control for a Rotary Inverted Pendulum (RIP) has been improved by using chaos synchronization with adding a chaotic signal as a reference. Rotary inverted pendulum is a nonlinear, under-actuated, unstable and non-minimum-phase system. The proposed control consists of a state-feedback (LQR) and a fuzzy-PID control. The state- feedback control is used to stabilize system near the operating point, and the fuzzy-PID is used to track the chaos signal. PID controller gains adjust by fuzzy rule. The designed controller is implemented on a Quanser laboratory system.

In this study, first by using the collected real data from a 10000 cubic-meter Qazvin-kowsar water supply reservoir is modeled by nonlinear output error (NOE) structure, then a neural nonlinear controller based on the MLP neural network according to created model is designed in order to control the tank water level. The operation of the proposed controller is compared by a PID controller which its coefficients is optimized by genetic algorithm. Results of the simulation indicates that the neural nonlinear controller has a better function than the PID controller, and also this controller is able to control the level water of the tank appropriately regardless the consumer profile in all conditions even in consumer picks.

This study proposes a novel chaotic anti-control for flexible joint system. The proposed controller is composed of a Lyapunov rule-based fuzzy control and chaotic anti-control for target tracking of the flexible joint manipulator. Chaotic signal is used to study the effect of anti-control to reduce the deflection of flexible joint system and control signal energy. For this purposes the flexible joint has been synchronized with chaotic Lorenz system. In this study on of the Lorenz parameters is changed to analysis the effect of chaotic signals. The results of the proposed approach shows in terms of level of vibration reduction and energy consumption of control signal, we could find an optimum point based on value of Lorenz system parameter. Finally, the efficacy of the proposed method and results of existence of different nonlinearity behavior is validated through experiments on QUANSER's flexible-joint manipulator.

In this paper, modeling, identification and control of a real 162MW heavy duty industrial gas turbine is taken into account. This work is aimed to introduce a simple and comprehensive model to test various controllers. Rowen's model is used to present the mechanical behavior of the gas turbine, while the identification of it is done using a feedforward neural network. The control rules of the turbine are applied on both models and a comparison of the results is also presented.

In this paper, obtaining of maximum active and reactive output power for wind turbines equipped with a double fed induction generator using stator-flux-oriented vector control based on novel multivariable input output linearization sliding mode control presented. The main control problem is the estimation of maximum power operating points of wind turbine under stochastic wind velocity profiles and tracking them using conventional offline and innovative adaptive online method. In this control strategy the wind speed and consequent aerodynamics torque is considered as the disturbance. Results under different operating conditions show the superior performance of the proposed online input-output linearization sliding mode technique.

It has been known that, real right half plane (RHP) zeros imply serious limitations on the performance of nonminimum phase systems. Feedback cannot remove these limitations, mainly because RHP zeros cannot be cancelled by unstable poles of the controller since such a cancellation leads to internal instability. Hence, the idea of using fractional order systems in partial cancellation of the RHP zeros without leading to internal instability is studied. In this paper, the partial cancellation of RHP zeros with RHP poles is proposed using the fractional calculus approach. It is shown that undershoot and settling time of the compensated system is improved. Using suitable optimum criterion, it is shown that the performance of closed loop system can be relatively improved. Simulation results are used to show the effectiveness of the proposed methodology.

This study applies technique PDC (parallel distributed compensation) for speed control of a Digital Servo System. PDC method is based on nonlinear Takagi-Sugeno (TS) fuzzy model. Also in this study Neural Adaptive is used for velocity control and identification of a Digital Servo System. It is shown that these techniques can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by experimental and computer simulation. The controllers which introduced have big range for control the system. We compare PDC controller with Neural Adaptive controller results and PID controller.

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.

In this paper, a new approach to the problem of stabilizing a chaotic system is presented. In this regard, stabilization is done by sustaining chaotic properties of the system. Sustaining the chaotic properties has been mentioned to be of importance in some areas such as biological systems. The problem of stabilizing a chaotic singularly perturbed system will be addressed and a solution will be proposed based on the OGY (Ott, Grebogi and Yorke) methodology. For the OGY control, Poincare section of the system is defined on its slow manifold. The multi-time scale property of the singularly perturbed system is exploited to control the Poincare map with the slow scale time. Slow scale time is adaptively estimated using a parameter estimation technique. Control with slow time scale circumvents the need to observe the states. With this strategy, the system remains chaotic and chaos identification is possible with online calculation of lyapunov exponents. Using this strategy on ecological system improves their control in three aspects. First that for ecological systems sustaining the dynamical property is important to survival of them. Second it removes the necessity of insertion of control action in each sample time. And third it introduces the sufficient time for census.

The idea that chaos could be a useful tool for analyze nonlinear systems considered in this paper and for the first time the two time scale property of singularly perturbed systems is analyzed on chaotic attractor. The general idea introduced here is that the chaotic systems have orderly strange attractors in phase space and this orderly of the chaotic systems in subscription with other classes of systems can be used in analyses. Here the singularly perturbed systems are subscripted with chaotic systems. Two time scale property of system is addressed. Orderly of the chaotic attractor is used to analyze two time scale behavior in phase plane.

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.

This paper presents a new approach for tuning PID controller parameters in the control of nonlinear systems. The design is based on optimal tracking of step response for nonlinear systems. The problem is first restated as a non linear optimal control infinite horizon problem, then with a suitable change of variable, the time interval is transferred to the finite horizon [0 1). This change of variable, poses a time varying problem. This problem is then transferred to measure space, and it is shown that an optimal measure must be determined which is equivalent to a linear programming problem with infinite dimension. Then, using finite horizon approximations, the optimal control law as piece wise constant function is determined. Finally, PID controller parameters are Determined using the optimal control law. Simulations are provided to show the effectiveness of the proposed methodology.

Various studies have been devoted to modulation and control of power electronic systems. Modeling of such a system is often required for control purposes. One modeling approach is the standard state space average model (SSSAM), which considers switching behaviors of the converters. The developed SSSAM of the static compensators (STATCOM) describes a non-affine model that is hardly controllable. A decomposition procedure has been proposed in this paper to make this non-affine SSSAM like an affine model. First, a non-affine SSSAM is derived that includes an interconnected STATCOM to an equivalent Thevenin model of the network along with the load. Then, the proposed decomposition procedure is applied to the non-affine SSSAM, where the resultant affine SSSAM is simulated. Simulations are presented for both the non-affine and the proposed affine model, showing the performance of the proposed procedure.

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.

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.

Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.

Overhead crane is an industrial structure that is widely used in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.

One of the common industrial structures that are used widely in many harbors and factories and buildings is overhead crane. Overhead cranes are usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane, a fuzzy controller designed with parallel distributed compensation and Linear Quadratic Regulation. With the Takagi-Sugeno fuzzy modeling, the nonlinear system is approximated by the combination of several linear subsystems in the corresponding fuzzy state space region. Then by constructing a linear quadratic regulation subcontroller according to each linear subsystem, a parallel distributed fuzzy LQR controller is designed. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. Simulation results illustrated the validity of the proposed control algorithm and it is compared with a similar method parallel distributed fuzzy controller.

Recently a lot of works have been done to detect faults in nonlinear systems. In this paper a new method, based on parity relations for linear systems, is proposed to detect faults in nonlinear systems that can be modeled by Takagi-Sugeno (TS) fuzzy system. This method is an intuitive generalization of parity relations, because TS fuzzy system uses local linear models. Results of simulation and implementation on a rotary inverted pendulum show that faults can be detected very well.

This paper aims to increase the classification specificity by using multi classifier system. First, a novel pixel search approach is applied to find significant region in images. Fuzzy C-means is utilized to determine the clear boundary of tumor. Then, shape and texture features are extracted from region of interest. Genetic algorithm is applied to select the best feature used for classifiers. Several neural networks and support vector machine are considered as classifiers that classify the data into benign and malignant group. To improve the performance of classification, three classifiers that have the best results among all applied methods are combined together that they have been named as multi classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results are achieved from two classifiers of multi classifier system. Notice that the Jack-Knife technique is applied in this study, because it is useful for small data base as ours gotten from Milad Hospital in Tehran, Iran.

In this study, Extended Kalman Filter (EKF) algorithm is developed to estimate the parameters of Hammerstein-Wiener (HW) ARMAX models. The basic idea is to estimate the original parameters of the identification model, which are appeared in the form of product terms, directly. While, other algorithms like Extended Forgetting Factor Stochastic Gradient (EFG), Extended Stochastic Gradient (ESG), Forgetting Factor Recursive Least Square (FFRLS) and Kalman Filter (KF), estimate parameters in the product form and they need another algorithms such averaging method (AVE method), singular value decomposition method (SVD method) to separate the parameters. So, the computational complexity of the proposed approach decreases. To show the efficiency of this method the results are compared with EFG and ESG method.

This study proposed a model based fault detection and isolation (FDI) method using multi- layer perceptron (MLP) neural network. Detection and isolation of realistic faults of an industrial gas turbine engine in steady-state conditions is mainly centered. A bank of MLP models which are obtained by nonlinear dynamic system identification is used to generate the residuals, and also simple thresholding is used for the intend of fault detection while another MLP neural network is employed to isolate the faults. The proposed FDI method was tested on a single-shaft industrial gas turbine prototype and it have been evaluated using non-linear simulations based on the real gas turbine data. A brief comparative study with other related works in the literature on this gas turbine benchmark is also provided to show the benefits of proposed FDI method.

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).

In this paper, a Genetic-AIS (Artificial Immune System) algorithm is introduced for PID (Proportional-Integral-Derivative) controller tuning using a multi-objective optimization framework. This hybrid Genetic-AIS technique is faster and accurate compared to each individual Genetic or AIS approach. The auto-tuned PID algorithm is then fused in an Immune feedback law based on a nonlinear proportional gain to realize a new PID controller. Immune algorithm presents a promising scheme due to its interesting features such as diversity, distributed computation, adaptation and self monitoring. Accordingly, this leads to a more effective Immune-based tuning than the conventional PID tuning schemes benefiting a multi-objective optimization prospective. Integration of Genetic-AIS algorithm with Immune feedback mechanism results into a robust PID controller which is ultimately evaluated via simulation control test scenarios to demonstrate quick response, good robustness, and satisfactory overshoot and disturbance rejection characteristics.

In this paper control of the xy pedestal axes has been studied which is a two degrees of freedom earth station antenna of pedestal HDF family group (High Dynamic Full Motion Leo Satellite Tracking Pedestals). This system model is achieved by using the Dymola software that according to the comparisons which have been done is very close to the actual system model and has very high accuracy. Purpose: is to track a LEO orbit satellite that of passing satellites and angles related to the antenna have been extracted path from KNTUSAT software. In carried out simulation, the operation of PI controller has been optimized, MPC and GPC has been studied. The results of comparison between simulation methods show that predictive controller has had fewer errors in satellite tracking and has been shown less control effort and also has had good behavior in disturbance rejection.

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.

In this study, a new type of training the adaptive network-based fuzzy inference system (ANFIS) is presented by applying different types of the Differential Evolution branches. The TSK-type consequent part is a linear model of exogenous inputs. The consequent part parameters are learned by a gradient descent algorithm. The antecedent fuzzy sets are learned by basic differential evolution (DE/rand/1/bin) and then with some modifications in it. This method is applied to identification of the nonlinear dynamic system, prediction of the chaotic signal under both noise-free and noisy conditions and simulation of the two-dimensional function. Instead of DE/rand/1/bin, this paper suggests the complex type (DE/current-to-best/1+1/bin & DE/rand/1/bin) on predicting of Mackey-glass time series and identification of a nonlinear dynamic system revealing the efficiency of proposed structure. Finally, the method is compared with pure ANFIS to show the efficiency of this method.

In this paper, a robust adaptive controller for accommodation of partial actuator faults is introduced. The proposed controller is based on the robust adaptive model-reference control scheme with improved dead zone modification. The common problem of steady state error in robust adaptive systems with simple dead zone modification is resolved by tuning a specific parameter inside the dead zone with a different adaptive law. Different types of actuator faults including output offset, loss of efficiency, and output delay are compensated with the proposed method. We behave these faults as uncertainties and disturbances. The proposed technique does not need an extra unit for fault detection and diagnosis. Comparative simulation studies are performed to illustrate the effectiveness of the proposed control technique versus the robust adaptive controller with simple dead zone modification.

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.

This paper presents a new tuning strategy for Generalized Predictive Controllers (GPC) based on Analysis of Variance (ANOVA). This strategy is derived for Second Order plus Dead Time (SOPDT) models of an industrial plant. Moreover, SOPDT modeling allows oscillating modes to be included in the model dynamics. The tuning strategy employs a simple expression for the tuning parameter as a function of plant parameters which is absent in earlier tuning attempts. This novel expression is extracted using ANOVA method combined with nonlinear regression. Also, a better performance index value and more convenient implementation are obtained in comparison with the conventional GPC tuning methods. Therefore, the tuning strategy for SOPDT models is both more comprehensive and more effective than traditional First Order plus Dead Time (FOPDT) model tunings. The proposed strategy is verified by two comparative simulation studies.

Expanding mathematical models and forecasting the traffic flow is a crucial case in studying the dynamic behaviors of the traffic systems these days. Artificial Neural Networks (ANNs) are of the technologies presented recently that can be used in the intelligent transportation system field. In this paper, two different algorithms, the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) have been discussed. In the training of the ANNs, we use historic data. Then we use ANNs for forecasting a daily real time short-term traffic flow. The ANNs are trained by the Back-Propagation (BP) algorithm. The variable coefficients produced by temporal signals improve the performance of the BP algorithm. The temporal signals provide a new method of learning called Temporal Difference Back-Propagation (TDBP) learning. We demonstrate the capability and the performance of the TDBP learning method with the simulation results. The data of the two lane street I-494 in Minnesota city are used for this analysis.

In this paper, a new feature selection and classification methods based on artificial neural network are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI). The database including benign and malignant lesions is specified to select the features and classify with proposed methods. It is collected from 2004 to 2006.

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.

Gas turbines are used widely in power generation, oil and gas industries, process plants and aviation. Efficiency and reliability is crucial in such applications. Hence, accurate modeling and control system designing is necessary. This paper first presents a nonlinear modeling of a single-shaft gas turbine in power generation application. This model is developed by solving differential and algebraic thermo dynamic equations and using turbine's component maps. Using this complex model, a number of linear models is identified around turbine's operating points. Effect of frequency and ambient condition is also considered in the models. Comparing these models, reduced number of linear models is selected to cover turbine's entire operating range. These models are validated using further identification tests and nonlinear model responses.

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.

In this research, a semi active control system with continuous variations along with hydro active dampers and springs is developed for a passenger car. The improvement of dynamic behavior of a passenger car with regard to weight constraint, energy consumption and cost highlights the need for the employment of such a semi active suspension system. Here, a full car model with hydro active subsystems including roll, pitch, bounce movements, and one degree of freedom for the driver is extracted, unlike the previous research in which merely the bouncing motion has been taken into account. By using the linearized car model equipped with the proposed hydro active system, the optimal damping force based on full state feedback control and LQR method is obtained for the improvement of the ride comfort and stability. In addition, practical constraints on manufacturing of the components and delay in the control system are dealt with. The car is excited by a disturbance such as a bump imparted to the wheels for which the idea of the wheel based filtering was also considered. The simulation results in the time domain for the hydro active suspension system demonstrate significant improvements in all controlled modes in comparison with the passive system. On the other hand, in comparison with the fully active system, the proposed system has an additional advantage in terms of energy consumption and weight reduction in the required hardware.

One of the most common cardiovascular diseases is Myocardial Ischemia (MI). The aim of this study is improving the diagnosis level of Ischemic Beat detection from ECG signals which is important in ischemic episode detection process. This improvement is based on appropriate feature extraction via Multi resolution Wavelet analysis and proper classifier selection. The approach starts with signal preprocessing, Afterwards efficacious morphologic features which are useful in ischemia detection are extracted by wavelet analysis. In the third stage subtractive clustering is performed for clustering. Finally probabilistic neural networks are used as a classifier. The proposed algorithm is evaluated on the European Society of Cardiology (ESC) ST-T database and reported 96.67% sensitivity and 89.18% specificity.

To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of traffic systems. METANET model is one of the most applicable models in traffic modeling which parameters have plenty of effects on model behavior. In this paper, we describe the effects of the model parameters on the model behavior and the estimation quality of system states in the case of undetermined parameters. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic traffic networks for preparing proper signal in traffic control.

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.

This paper proposes a hybrid control scheme for the synchronization of two chaotic nonlinear gyros, subject to uncertainties and external disturbances. In this scheme, Linear Quadratic Regulation (LQR) control, Sliding Mode (SM) control and Gaussian Radial basis Function Neural Network (GRBFNN) control are combined. By Lyapunov stability theory, SM control is presented to ensure the stability of the controlled system. GRBFNN control is trained during the control process. The learning algorithm of the GRBFNN is based on the minimization of a cost function which considers the sliding surface and control effort. Simulation results demonstrate the ability of the hybrid control scheme to synchronize the chaotic gyro systems through the application of a single control signal.

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.

In this paper, design of decentralized switching control for uncertain multivariable plants based on the Quantitative Feedback Theory (QFT) is considered. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model. It is assumed that a MIMO-QFT controller exists for robust stability and performance of the individual uncertain sets. The proposed control structure is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the local models behaviors with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching to guarantee the overall closed loop stability. It is shown that this strategy provides a stable and robust adaptive controller to deal with complex multivariable plants with input-output pairing changes during the plant operation, which can facilitate the development of a reconfigurable decentralized control. Simulation results are employed to show the effectiveness of the proposed method.

Past work on face detection has emphasized the issues of feature extraction and classification, however, less attention has been given on the critical issue of feature selection. We consider the problem of face and non-face classification from frontal facial images using feature selection and neural networks. We argue that feature selection is an important issue in face and non-face classification. Automatic feature subset selection distinguishes the proposed method from previous face classification approaches. First, Principal Component Analysis (PCA) is used to represent each image as a feature vector (i.e., eigen-features) in a low-dimensional space, spanned by the eigenvectors of the covariance matrix of the training images (i.e., coefficients of the linear expansion).Then we consider Linear Discrimination Analysis (LDA) to achieve a comparison result between these two methods of dimension reduction. Genetic Algorithm (GA) is then used to select a subset of features from the low-dimensional representation by removing certain eigenvectors that do not seem to encode important information about face. Finally, a Probabilistic Neural Network (PNN) is trained to perform face classification using the selected eigen-feature subset. Experimental results demonstrate a significant improvement in error rate reduction.

Double Inverted Pendulum is a nonlinear system, unstable and fast reaction system. Double Inverted Pendulum is stable when its two Pendulums allocated in vertically position and have no oscillation and movement and also insertion force should be zero. The main target of this research is design a controller based on Neuro-Fuzzy methods by using feedback- error-learning for controlling double inverted Pendulum.

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.

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.

This paper suggests a novel approach for control of a flexible-link based on the feedback- error-learning (FEL) strategy. A radial basis function neural network (RBFNN) is used as an adaptive controller to improve the performance of a lead compensator controller in FEL structure. This scheme is developed by using a modified version of the FEL approach to learn the inverse dynamic of the flexible manipulator which requires only a linear model of the system for designing lead compensators and RBFNN controllers. The final controller should allow the user to command a desired tip angle position. The controller should eliminate the link's vibrations while maintaining a desirable level of response. Finally, the control performance of the proposed control approach for tip position tracking of flexible-link manipulator is illustrated by simulation result.

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In this paper a Mamdani type fuzzy system and an adaptive network based fuzzy inference system (ANFIS) are presented for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. The architecture and learning procedure ANFIS is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. It is shown that both these controllers can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of nonlinear mathematical model of the system.

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.

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.

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.

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.

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.

In This paper, a new adaptive controller based on modified feedback error learning (FEL) approaches is proposed for load frequency control (LFC) problem. The FEL controller consists of neural network feedforward controller (NNFC) and conventional feedback controller (CFC), where the CFC is essential to guarantee global asymptotic stability of the overall system. Also, for improved the performance of system the dynamic neural network (DNN) is adopted in NNFC instead of conventional neural network. This neural network has dynamic in its structure and consists of two units: inhibitory and excitatory unit. The proposed FEL controller has been compared with the conventional FEL (CFEL) controller and the PID controller through some performance indices.

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.

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.

In this paper designing of multi-objective PID controller for load frequency control (LFC) based on adaptive weighted particle swarm optimization (AWPSO) has been proposed. Conventional methods such as Ziegler-Nichols and Cohen-Coon are based on trial-and- error and their best performances are achieved for first-order process. Single-objective population based methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have only one solution in a single run. Unlike single objective methods, multi- objective optimization can find different solutions in a single run. In the proposed method, overshoot/undershoot and settling time are used as objective functions for multi-objective optimization. The proposed method is used for designing of PID parameters for two area interconnected power system.

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.

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.

In this study integer genetic algorithm is applied for path planning of mobile robot in the grid form environment. The novel representation is proposed for definition of chromosome which reduced the computational complexity of genetic algorithm that was used before for path planning. Comparison with other encoding of chromosome is done to show the capability of proposed algorithm. Mamadani fuzzy rule is used to describe difficulty of passing from cells which are sandy or have slope.

This paper, describes a hybrid control method to control a flexible joint. Dynamic equation of the system has been derived. The designed controllers consist of two parts: classical controller, which is a Linear Quadratic Regulation (LQR), and a hybrid controller, utilizing sliding mode control using Gaussian Radial Basis Function Neural Networks (RBFNN). The RBFNN is trained during the control process and it is not necessary to be trained off-line.

This study suggests new learning laws for Adaptive Network based Fuzzy Inference System that is structured on the basis of TSK type III as a system identifier. Stable learning algorithms for consequence parts of TSK type III rules are proposed on the basis of the Lyapunov stability theory and some constraints are obtained. Simulation results are given to validate the results. It is shown that instability will not occur for learning rates in the presence of constraints. The learning rate can be calculated online from the input–output data, and an adaptive learning for the Adaptive Network based Fuzzy Inference System structure can be provided.

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.

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 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.

In this paper, we discuss a neural network based on hebbian learning rule for finding the inverse of a matrix. First we described finding the inverse of a matrix by mentioned neural network. Finally, experimental results for square and non-square matrices are presented to show the effectiveness of the approach. Proposed method is also scalable for finding the inversion of large-scale matrices.

This paper present power system load frequency control by modified dynamic neural networks controller. The controller has dynamic neurons in hidden layer and conventional neurons in other layers. For considering the sensitivity of power system model, the neural network emulator used to identify the model simultaneously with control process. To have validation of proposed structure of neural network controller the results of simulation demonstrated that the proposed controller offers better performance than conventional neural network controller.

This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). This approach based on multi objective optimization mechanism for training parameters in antecedent part. It considers two cost functions as the objectives which are the maximum difference measurements between the real nonlinear system and the nonlinear model, and training mean square error (MSE). The NSGA-II is the multi objective optimization algorithm which employed for this purpose. So we use gradient decent (GD) method for training all parameters in conclusion part. Finally we show simulation results of applied this method to some nonlinear identification system.

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.

Abstract Simulation of bending vibration effects on a two-stage launch vehicle and design of a new adaptive algorithm to reduce the flexible behaviours are discussed in this paper. The new adaptive algorithm uses recursive least square (RLS) method and two notch filters for decoupling rigid and flexible dynamic of the launch vehicle, estimating the bending frequency and reducing vibration effects. Applying this adaptive controller to the launch vehicle control system satisfied all design requirements. As the designed adaptive controller decouples rigid and flexible dynamics for estimating the bending frequency, it is simpler and faster than the other approaches and uses less CPU-capacity. The proposed approach validated by developing 6DoF nonlinear simulation software.

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.

This study addresses new hybrid approaches for velocity control of an electro hydraulic servosystem (EHSS) in presence of flow nonlinearities and internal friction. In our new approaches, we combined classical method based-on sliding mode control and fuzzy RBF networks. The control by using adaptive networks need plant's Jacobean, but here this problem solved by sliding surface. It is demonstrated that this new technique have good ability control performance. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of a nonlinear mathematical model of the system. The controllers which introduced have big range for control the system.

This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with gradient decent (GD) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from genetic algorithm (GA) method. The simulation results show that in comparison with current GD training, the novel training can have a better adaptation to complex plants. Also, the results show this new hybrid approach optimizes ANFIS parameters faster and better parameters than gradient base method

This paper introduces a new approach for routing in telecommunication networks. In this approach some theoretical foundations from mathematical modeling theory and integer programming have been exploited to develop a framework for routing problems. Some binary variables are assigned to the network links and for each link the corresponding binary variable shows the presence of the corresponding link on a specified route. The optimal route is determined in the source router per connection request by optimization of an objective function. An estimate of the residual bandwidth of network links is maintained in the source router. This information is used in the optimization problem to select the best available route from the source router to the destination router based on the selected metric. Required characteristics of a route are specified as logical constraints on the optimization variables. By using some tools from mathematical modeling theory, these logical constraints are transformed into equivalent integer linear inequalities. This technique results in a well-defined integer linear programming optimization problem

Today satellites have important role in all parts of human living. For satellite correct communication should track satellite and so satellite antenna or camera should track Earth station correctly. Both correct attitude control and attitude determination are two factors to tracking from satellite. By using attitude control simulator could simulate attitude of satellite in each point of orbit with each disturbance. In this paper design of a satellite simulator by different methods of control and determination is investigated and some results are presented

In this paper a novel hybrid strategy is employed in order to improve the controller performance. The main idea is combination of classical and intelligent controllers. Feedback error learning (FEL) as a two degrees of freedom (2DOF) control scheme, has been introduced based on this idea. This paper takes a step ahead of traditional FEL schemes which combine a PID controller with an intelligent inverse based controller. We introduce a robust FEL scheme and the robust controller replaces the conventional PID controller. The Robust controller is designed based on the Hinfin approach and the intelligent controller has ANFIS structure. This novel algorithm is implemented in a Flow plant to track the desired value of flow and reject unwanted disturbances in the practical system. The results are brought to prove the practical power of the novel method and are compared with other control schemes.

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

Quantitative Feedback Theory (QFT) is one of most effective methods of robust controller design and can be considered as a suitable method for systems with parametric uncertainties. Particularly it allows us to obtain controllers less conservative than other methods like H∞ and µ-synthesis. In QFT method, we transform all the uncertainties and desired specifications to some boundaries in Nichols chart and then we have to find the nominal loop transfer function such that satisfies the boundaries and has the minimum high frequency gain. The major drawback of the QFT method is that there is no effective and useful method for finding this nominal loop transfer function. The usual approach to this problem involves loop-shaping in the Nichols chart by manipulating poles and zeros of the nominal loop transfer function. This process now aided by recently developed computer aided design tools proceeds by trial and error and its success often depends heavily on the experience of the loop-shaper. Thus for the novice and First time QFT user, there is a genuine need for an automatic loopshaping tool to generate a first-cut solution. In this paper, we approach the automatic QFT loop-shaping problem by using a procedure involving Linear Programming (LP) techniques and Genetic Algorithm (GA).

This paper discusses the design of a cascade controller for active suspension systems, to improve ride quality. In order to do this, in the main loop, a model reference adaptive controller is designed to attenuate disturbances due to rough roads. An internal loop provides the required control force for the main controller. The closed loop system has desired robust stability and performance in the presence of uncertainty due to time varying parameters and nonlinear dynamics of the actuator. The simulation results show the effectiveness of the suggested method in increasing ride comfort and safety while constrains of suspension system maneuverability is also satisfied

In this paper, generalized predictive control (GPC) algorithm is implemented to control an earth station antenna. Nonlinear term in motors caused by gearbox or other parts is modeled by a backlash block. Simulation results show the effectiveness of GPC method for robust control in the presence of backlash nonlinearity without a priori knowledge about upper and lower bounds of backlash. Also, adaptation mechanism as a self tuning predictive control is used to conquer environment changing

In this paper, generalized predictive control (GPC) algorithm is implemented to control an Earth station antenna with a non-minimum phase motor. Nonlinear term in motors caused by gearbox or other parts is modeled by a backlash block. Simulation results show the effectiveness of GPC method for robust control in the presence of backlash nonlinearity without a priori knowledge about upper and lower bounds of backlash. Also, adaptation mechanism as a self tuning predictive control is used to conquer environment changing

Current state-of-the-art approaches for control of hybrid systems face with two main important challenging problems which are guaranteeing the stability and the computational complexity. In this article a new approach has been proposed to guarantee the closed loop stability of a class of hybrid systems while reducing the complexity of control problem by introducing some level of suboptimality. It has been shown that using contraction constraint on the objective function results in asymptotically stable closed loop system. It has also been described that since only feasibility is sufficient for stability in the proposed approach, suboptimal control could be used to reduce the computational complexity.

In this paper we propose a new data fusion method based on particle filtering and fuzzy logic in order to adaptively integrate global positioning system and strapdown inertial navigation system (GPS/SDINS). This approach will reduce the dependence of the stable solution on stochastic properties of the system which is a function of vehicle dynamics and environmental conditions So the proposed scheme will enhance the estimation performance in comparison with generic particle filter specially in the case of facing modeling uncertainty. It will also give us more reliable solution when encountering satellite signal blockage as a probable problem in land navigation. The results have clearly demonstrated that the hybrid fuzzy particle filter would improve the guidance from the point of accuracy and robustness to the mentioned problems.

A new approach to adaptive control of chaos in non-linear discrete time systems with delayed state feedback is presented. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, Lyapunov exponents are used to select the controller gain. An effective adaptive strategy for on-line identification of Lyapunov exponents is proposed. Simulation results are provided to show the effectiveness of the proposed methodology.

Mixed logical dynamical (MLD) modeling appears as an effective and realistic approach in modeling and control of hybrid systems. In this modeling approach, dynamical and logical constraints as well as control system design specifications are transformed into so-called mixed-integer inequalities. In this paper, the MLD framework is used for modeling of a multi-tank system as a switched nonlinear system. Control of fluid levels in multiple tanks is considered as a case study for predictive control of MLD systems. Translation of control problem specifications into mixed-integer inequalities shows the ability of MLD framework to deal with complex modeling and optimization tasks

Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, a method using a hybrid-adaptive network based fuzzy inference system (ANFIS) has been proposed which is trained during the availability of GPS signal to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with respect to the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS integration in comparison with conventional ANFIS specially in the cases of satellites’ outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.

This paper presents a novel approach to solve the MIMO-QFT problem for tracking error specification through a method of obtaining exact bounds for the design of individual elements of pre-filter. The paper specifically deals with the appropriate transformation of the MIMO system to the equivalent SISO problems, which allows easy design to find the feedback compensator and pre-filter. A linearized model of quadruple-tank process is used to show the effectiveness of the proposed method.

One of the most important problems which affect the performance of active suspension system is variation in suspension parameters such as tire stiffness, mass of body, etc. In this paper two robust approaches are applied to active suspension: H/sub /spl infin// and QFT. The performance of these two controllers is examined in the presence of parameter variation and actuator nonlinear dynamics. Simulation results show that QFT is effective in the robust control of active suspensions in automobiles and so it is useful for automobile engineers to think about using this algorithm in automobiles.

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.

This paper proposes a reconfigurable controller design method in the case that full state feedback is not permissible. A new sufficient condition to guarantee the stability of output feedback reconfigurable controller is suggested. Based on the new condition, an algorithm is introduced that preserves much of the dynamics of the original unfailed system using eigenstructure assignment and genetic algorithms. The new algorithm guarantees the closed-loop stability of the reconfigured system.

In this paper, the problem of Lyapunov Exponents (LEs) computation from chaotic time series based on Jacobian approach by using polynomial modelling is considered. The embedding dimension which is an important reconstruction parameter, is interpreted as the most suitable order of model. Based on a global polynomial model fitting to the given data, a novel criterion for selecting the suitable embedding dimension is presented. By considering this dimension as the model order, by evaluating the prediction error of different models, the best nonlinearity degree of polynomial model is estimated. This selected structure is used in each point of the reconstructed state space to model the system dynamics locally and calculate the Jacobian matrices which are used in QR factorization method in the LEs estimation. This procedure is also applied to multivariate time series to include information from other time series and resolve probable shortcoming of the univariate case. Finally, simulation results are presented for some well-known chaotic systems to show the effectiveness of the proposed methodology.

In this paper, a mixed H/sub 2//H/sub /spl infin// controller is designed for 1/4 car suspension model. Neither H/sub 2/ nor H/sub /spl infin// controllers can provide goals of active suspension separately (i.e. minimizing body vertical acceleration considering restriction on suspension displacement). So, in this paper both objectives are considered in a mixed H/sub 2//H/sub /spl infin// problem. The Riccati equations are solved using a recursive algorithm and the mixed H/sub 2//H/sub /spl infin// controller performance is compared with H/sub 2/ and H/sub /spl infin// controllers through frequency and time domain simulations.

Quantitative Feedback Theory (QFT) is one of the most effective methods of robust controller design. In QFT design, we can consider the phase information of the perturbed plant so it is less conservative than H∞ and µ-synthesis methods. In this paper, we want to overcome the major drawback of QFT method, i.e., lack of an automated technique for loop-shaping. Clearly such an automatic process must involve some sort of optimization, and while recent results on convex optimization have found fruitful applications in other areas of control theory we have tried to use LMI theory for automating the loop-shaping step of QFT design.

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.

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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.

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.

Marine vehicles, and hydrofoils in particular, are complex multivariable plants with significant levels of open-loop interaction. It is difficult to obtain explicit mathematical models for such vehicles, and the effort involved is frequently wasted because marine vehicles usually exhibit significant plant-parameter variations. There is therefore a requirement for a methodology for the design of digital controllers for marine vehicles which is simply applicable to highly interactive multivariable plants, does not require explicit mathematical models, and is equally applicable to both fixed-parameter and variable-parameter plants. In order to circumvent the need for mathematical models in either state space or transfer function matrix form, and to avoid performance degradation, Jones and Porter (1987) introduced adaptive digital PID controllers. Such adaptive controllers incorporate online recursive identifiers which provide updated step-response matrices for inclusion in control laws. The effectiveness of these controllers for marine vehicles is illustrated by designs of both tunable and adaptive digital set-point tracking PID controllers for a four-input/four-output dynamically complex hydrofoil.