Ali Khaki-Sedigh

This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

In this paper, a recurrent neural network coupled with Kalman filter is proposed to identify dynamic terms of robotic manipulator. By cooperating some inherent characteristics of robot, this network has the capability to individually identify nonlinear terms using Weighted Augmentation Error (WAE). To present the infrastructure of architecture, an adaptive scheme based on the conventional Back Propagation (BP) is firstly driven using the Gradient Descent (GD) method. Additionally, a stable adaptive updating rule is extracted from the discrete time Lyapunov candidate as an approach for the general nonlinear system identification. Then, this approach is applied to the predefined network. To experimentally validate the computational efficiency and control applicability of the proposed method, Adaptive Neural Network Based Inverse Dynamic Control (ANN-Based-IDC) is employed on a laboratory-scaled twin-rotor CE-150 helicopter. This experiment illustrates enhancement of steady-state performance from 2-to-3 times more in compared with simple PID. Moreover, disturbance rejection and robustness tests admit capability of the method for online dynamic identification in the presence of output and dynamic perturbation.

This paper investigates the problem of decentralized model reference adaptive control (MRAC) for a class of large scale systems with time varying delays in interconnected terms and state and input delays. The upper bounds of the interconnection terms are considered to be unknown. Time varying delays in the nonlinear interconnection terms are bounded and nonnegative continuous functions and their derivatives are not necessarily less than one. Moreover, a simple and practical method based on periodic characteristics of reference model is established to predict the future states and input delay compensation. It is shown that the solutions of uncertain large-scale time-delay interconnected system converge uniformly exponentially to a desired small ball. The effectiveness of the proposed approaches are illustrated by a numerical example and a chemical reactor system.

University ranking systems attempt to provide an ordinal gauge to make an expert evaluation of the university’s performance for a general audience. University rankings have always had their pros and cons in the higher education community. Some seriously question the usefulness, accuracy, and lack of consensus in ranking systems and therefore multidimensional ranking systems have been proposed to overcome some shortcomings of the earlier systems. Although the present ranking results may rather be rough, they are the only available sources that illustrate the complex university performance in a tangible format. Their relative accuracy has turned the ranking systems into an essential feature of the academic lifecycle within the foreseeable future. The main concern however, is that the present ranking systems totally neglect the ethical issues involved in university performances. Ethics should be a new dimension added into the university ranking systems, as it is an undisputable right of the public and all the parties involved in higher education to have an ethical evaluation of the university’s achievements. In this paper, to initiate ethical assessment and rankings, the main factors involved in the university performances are reviewed from an ethical perspective. Finally, a basic benchmarking model for university ethical performance is presented.

This paper proposes a model bank selection method for a large class of nonlinear systems with wide operating ranges. In particular, nonlinearity measure and H-gap metric are used to provide an effective algorithm to design a model bank for the system. Then, the proposed model bank is accompanied with model predictive controllers to design a high performance advanced process controller. The advantage of this method is the reduction of excessive switch between models and also decrement of the computational complexity in the controller bank that can lead to performance improvement of the control system. The effectiveness of the method is verified by simulations as well as experimental studies on a pH neutralization laboratory apparatus which confirms the efficiency of the proposed algorithm.

Measuring the contour boundary irregularities of skin lesion is an important factor in early detection of malignant melanoma. On the other hand, cancer is usually recognized as a chaotic growth of cells. It is generally assumed that boundary irregularity associated with biomedical images may be due to the chaotic behavior of its originated system. Thus, chaotic indices can serve as some criteria for classifying dermoscopy images. In this paper, a new approach is presented for extraction of Lyapunov exponent and Kolmogorov–Sinai entropy in the skin lesion images. This method is based on chaotic time series analysis. Converting the region of interest of skin lesion to a time series, reconstruction of system phase space, estimation of the Lyapunov exponents and calculation of Kolmogorov–Sinai entropy are the steps of the proposed approach. The combination of the largest Lyapunov exponent and Kolmogorov–Sinai entropy is selected as a criterion for distinction between melanoma and mole categories. Experiments on a set of dermoscopy images yielded a sensitivity of 100% and a specificity of 92.5% providing superior diagnosis accuracy compared to other related similar works.

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.

Control performance assessment techniques are widely studied and many performance assessment indices have been proposed. In this paper, a control performance assessment technique for multi-loop control systems is presented based on the decision fusion strategy. Since decisions based on individual indices can lead to erroneous results, decision fusion of different indices can improve the assessment accuracy, especially in multi- loop control systems in the presence of loop interactions. Performance assessment indices are individually evaluated and decisions based on these indices are fused. The results of simulation and practical implementation on series cascade control structures illustrate the effectiveness of the proposed algorithm.

This paper addresses the problem of velocity estimation for a class of uncertain mechanical systems. Using advantages of immersion and invariance technique with input– output filtered transformation, a proper immersion and dynamical auxiliary filter have been constructed in the designed estimator. Uniform global asymptotic convergence of the velocity estimator has been proved for the system with parametric uncertainties. In the presence of perturbations on the input and output, the performance analysis of the estimator has been theoretically investigated and illustrated by simulation results.

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, we present a new method for obtaining closed-form SDC matrices for synthesis of SDRE controller for non-affine nonlinear systems. Furthermore, we design SDRE controller with OCU method for proposed SDC form. Simulation results shows that proposed method for designing SDRE controller yields good tracking performance and smoothness of control signals. Robustness of the designed SDRE controller will be illustrated to a class of external disturbances.

Model predictive controller is widely used in industrial plants. Uncertainty is one of the critical issues in real systems. In this paper, the direct adaptive Simplified Model Predictive Control (SMPC) is proposed for unknown or time varying plants with uncertainties. By estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly calculated. The proposed method is validated via simulations for both slow and fast time varying systems. Simulation results indicate the controller ability for tracking references in the presence of plant’s time varying parameters. In addition, an analytical tuning method for adjusting prediction horizon is proposed based on optimization of the objective function. It leads to a simple formula including the model parameters, and an indirect adaptive controller can be designed based on the analytical formula. Simulation results indicate a better performance for the tuned controller. Finally, experimental tests are performed to show the effectiveness of the proposed methodologies.

In this paper, an analytical method for tuning the parameters of the set-point weighted fractional order PID (SWFOPID) controller is proposed. The studied control scheme is the filtered fractional set-point weighted (FFSW) structure. Also to achieve a desired closed-loop performance, a fractional order pre-filter is employed. The proposed method is applicable to stable plants describable by a simple three-parameter fractional order model. Such a model can be considered as the fractional order counterpart of a first order transfer function without time delay. Finally, the proposed method is implemented on a laboratory scale CE 150 helicopter platform and the results are compared with those of applying a filtered fractional order PI (FFOPI) controller in a similar structure. The practical results show the effectiveness of the proposed method.

Many industrial processes can be effectively described with first-order plus fractional dead time models. In the case of plants with a large dead time relative to the time constant, approximations in discretizing the time delay can adversely affect the performance and if the sample time is enforced by system requirements, the fractional nature of the delay should be considered. In this paper, an analytical approach to model predictive control tuning for stable and unstable first-order plus dead time models with fractional delay is presented. The existing tuning methods are based on trial and error or numerical optimization approaches and the available closed form equations are limited to plants with integer delays. In this paper, an analytical approach is adopted and the issues of closed loop stability and achievable performance are addressed. Finally, simulation results are used to show the effectiveness of the proposed tuning strategy.

In this paper, control performance assessment for a class of nonlinear systems modelled by autoregressive second-order Volterra series with a general linear additive disturbance is presented. The proposed approach employs the nonlinear generalised minimum variance (NGMV) controller concept. The Volterra series models provide a natural extension of a linear convolution model with the nonlinearity considered in an additive term. The polynomial operator form is used throughout this paper for the description of the system input–output model. The closed form formulation of NGMV controller for autoregressive second-order Volterra series is presented in a polynomial form then a control assessment criterion based on the NGMV control is given. Simulation results and comparison studies are used to show the effectiveness of the proposed approach for a class of nonlinear systems.

The main point of this paper is to present an iterative optimization strategy for tuning the parameters of Smith predictor based fractional order PID (SPFOPID) controller. The control scheme considered in this paper is the standard Smith predictor structure. Also, the internal model is considered to be a First Order Plus Dead Time (FOPDT) transfer function. Finally, the proposed method is implemented on a multi input-multi output (MIMO) flow-level plant and the obtained results are compared with the results of applying Smith predictor based PID controller (SPPID) in the similar structure.

The problem discussed in this paper is the effect of latency time on the OGY chaos control methodology in multi chaotic systems. The Smith predictor, rhythmic and memory strategies are embedded in the OGY chaos control method to encounter loop latency. A comparison study is provided and the advantages of the Smith predictor approach are clearly evident from the closed loop responses. The complex plants considered are coupled chaotic maps controlled by the extended OGY scheme. Simulation results are used to show the effectiveness of the applied Smith predictor scheme structure in multi chaotic systems.

Dynamic Matrix Control is a widely used Model Predictive Controller in industrial processes. The successful implementation of Dynamic Matrix Control in practical applications requires appropriate tuning of the controller parameters. Three different cases are considered. In the first case, a tuning formula is developed that ensures the nominal closed loop desired performance. However, this formula may fail in the presence of plant uncertainty. Therefore a lower bound for the tuning parameter is derived to secure the robust stability of the uncertain first order plus dead time plant. Finally, a tuning boundary is derived which gives the lower and upper permissible bounds for the tuning parameter that guarantee the robust performance of the uncertain first order plus dead time plant. The tuning procedure is based on the application of Analysis of Variance, curve fitting and nonlinear regression analysis. The derived results are validated via simulation studies and some experimental results.

This paper addresses the problem of output (angular position) feedback tracking control of two-degree-of-freedom X–Y pedestal systems. Both the velocity observer and the controller are based on a partial quasi-linearized model for the X–Y pedestal system. The two-dimensional velocity observer is uniformly globally exponentially convergent and does not require a priori upper-bound knowledge of the velocity magnitude. An important feature of the proposed observer is that it constructs a uniform global stable output feedback tracking controller with any domain of initial tracking errors and initial estimation errors. The proof of the main results is based on the well-established theorems for cascaded nonlinear time-varying systems. Due to uniform asymptotic stability of the observer and the output feedback controller, numerical simulations show their robust performance in the face of bounded additive perturbations on both input and output.

Multivariable model predictive control is a widely used advanced process control methodology, where handling delays and constraints are its key features. However, successful implementation of model predictive control requires an appropriate tuning of the controller parameters. This paper proposes an analytical tuning approach to multivariable model predictive controllers. The considered multivariable plants are square and consist of first-order plus dead time transfer functions. Most of the existing model predictive control tuning methods are based on trial and error or numerical approaches. In the case of no active constraints, closed loop transfer function matrices are derived and decoupling conditions are addressed. For control horizon of one, analytical tuning equations and achievable performances are obtained. Finally, simulation results are used to verify the effectiveness of the proposed tuning strategy.

Robustness of parameter estimator plays a vital role in adaptive controllers. A modified identification algorithm is proposed based on the augmented UD identification (AUDI) primary version. Augmented UD identification with selective forgetting (AUDSF) method is derived as a robust derivation of AUDI to be integrated with input-output data filtering, relative dead zone, and data normalisation features. AUDSF is incorporated by generalised predictive controller (GPC) strategy to produce an applicable adaptive control method. The comparative performances of the developed approach have been explored on two-mass spring challenging benchmark problem, which demonstrates its excellent behaviour under conducted parameter and disturbance uncertainty scenarios.

This paper addresses the problem of stabilizing a TORA system without velocity measurement. For this purpose, two classes of output feedback designs, direct and indirect, are employed to design a nonlinear observer for estimating an unavailable variable (velocity variable). Moreover, the theory of cascaded time-varying systems has been used to improve the indirect output feedback controller and to enable the independent tuning of the observer and the controller. The results of Lyapunov stability analysis show globally asymptotic stability of the system in closed loop using the output feedback controllers designed in this paper.

Model Predictive Controllers (MPC) are effective control strategies widely used in the industry. The desirable MPC performance requires appropriate tuning of the controller parameters. However, the MPC tuning parameters are related to the closed loop characteristics in a complex and nonlinear manner, so the tuning procedure is an intricate problem, which has received much attention in recent decades. In this paper, the effects of each tuning parameter on the closed loop behavior are studied. Then, the issue of MPC tuning problem is considered and a review of the available tuning methods are provided. Modern tuning strategies are also considered. The emphasis of this paper is on theoretical tuning strategies which lead to closed form tuning equations that can be used in closed loop analysis. Finally, a simulation study is employed to have a comparative study on some closed form tuning equations and the advantages and disadvantages of each method is clarified.

In this paper, an adaptive multiple model predictive controller (AMMPC) based on multiple model switching and tuning strategy and dynamic matrix control (DMC) system is presented to construct switching-tuning adaptive multiple model predictive controller (STAMMPC). Disadvantages of non adaptive multiple model predictive control (MMPC) in regulation and disturbance rejection are discussed and new robust adaptive supervisors to improve the decision making procedures are developed. Experimental results on pH neutralization process show that the proposed decentralized control strategy using STAMMPC algorithm has desirable performance and robustness characteristics and is superior to the other MMPC algorithms, especially in the case of the participation with suggested new adaptive disturbance rejection supervisor.

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.

An adaptive fault tolerant control systems are vital in many industrial systems. Redundancy is a practical approach to decrease the effects of faults in systems. Redundancy in actuators can also increase system reliability and flexibility. This paper proposes a fuzzy control allocation method that can allocate control signal among actuators to increase reliability and maneuverability in healthy conditions and tolerating faults in faulty conditions. Using fuzzy logic is an intelligent way to adaptively change the gains of control allocation in different operating conditions.

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.

In this paper, for a class of linear systems with unknown parameters, a direct model reference adaptive control scheme in output feedback form has been presented, which assures stable adaptation in the presence of input saturation. Also, under certain assumptions one can guarantee that the adaptive control signal will avoid input saturation. In addition, by considering that the error model is in a parametric model form, robust adaptive control is used to improve robustness of systems in the presence of bounded disturbances. This is achieved by using the a-modification method. Simulation of an output feedback system with relative degree 2 verifies the results given in the paper.

Information signal from real case and natural complex dynamical systems such as traffic flow are usually specified by irregular motions. Chaotic nonlinear dynamics approach is now the most powerful tool for scientists to deal with complexities in real cases, and neural networks and neuro-fuzzy models are widely used for their capabilities in nonlinear modeling of chaotic systems more than the traditional methods. As mentioned, the traffic flow conditions caused the forecasting values of traffic flow to lack robustness and accuracy. In this paper, the traffic flow forecasting is analyzed with emotional concepts and multi-agent systems (MASs) points of view as a new method in this field. The findings enabled the researchers to develop a newly object-oriented method of forecasting traffic flow. Its architecture is based on a temporal difference (TD) Q-learning with a neuro-fuzzy structure, which is the nonparametric approach. The performance of TD Q-learning is improved by emotional learning. The proposed method on the present conditions and the action of the system according to the criteria could forecast traffic signals so that the objectives are reached in minimum time. The ability of presented learning algorithm to prospect gains from future actions and obtain rewards from its past experiences allows emotional TD Q-learning algorithm to improve its decisions for the best possible actions. In addition, to study in a more practical situation, the neuro-fuzzy behaviors could be modeled by MAS. The proposed method (intelligent/nonparametric approach) is compared by parametric approach, autoregressive integrated moving average (ARIMA) method, which is implemented by multi-layer perceptron neural networks and called ARIMANN. Here, the ARIMANN is updated by backpropagation and temporal difference backpropagation for the first time. The simulation results revealed that the studied forecaster could discover the optimal forecasting by means of the Q-learning algorithm. Difficult to handle through parametric and classic methods, the real traffic flow signals used for fitting the algorithms is obtained from a two-lane street I-494 in Minnesota City.

Nowadays, genetic disorders, like cancer and birth defects, are a great threat to human life. Since the first noticing of these types of diseases, many efforts have been made and researches performed in order to recognize them and find a cure for them. These disorders affect genes and they appear as abnormal traits in a genetic organism. In order to recognize abnormal genes, we need to predict splice sites in a DNA signal; then, we can process the genetic codes between two continuous splice sites and analyze the trait that it represents. In addition to abnormal genes and their consequent disorders, we can also identify other normal human traits like physical and mental features. So the primary issue here is to estimate splice sites precisely. In this paper, we have introduced two new methods in using neuro-fuzzy network and clustering for DNA splice site prediction. In this method, instead of using raw data and nucleotide sequence as an input to neural network, a survey on the first bunch of the nucleotide sequence of true and false categories of the input is carried out and training of the neuro-fuzzy network is achieved based on the similarities and dissimilarities of the selected sequences. In addition, sequences of the large input data are clustered into smaller categories to improve the prediction as they are really spliced based on different mechanisms. Experimental results show that these improvements have increased the recognition rate of the splice sites.

In this paper input-output pairing is done based on concept of energy. Parseval theorem and cross-covariance samples of input-output are used for estimation of energy. After approximating interaction energy between input and output of the plant, input-output pairing is fulfilled. Through examples, it is illustrated that proposed method is appropriate for input-output pairing. The result is compared with Effective Relative Energy Array (EREA) as another energy based approach for input-output pairing.

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.

In this paper, the design of decentralized switching control for uncertain multivariable plants is considered. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model and specific control structure. The underlying design is based on the quantitative feedback theory (QFT). 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 decentralized 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 the 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. Also, the multirealization technique is used to implement a family of controllers to achieve bumpless transfer. Simulation results are employed to show the effectiveness of the proposed method.

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.

Minimal stopping distance, guaranteed steering ability and stability are the three most important purposes in Anti-lock Braking System (ABS) realm. The ABS system is a nonlinear, time variant and multivariable system with some uncertainties. Some research work has been carried out on ABS control systems using intricate methods, which are expensive to implement. In this paper at the first step, the system interference is decreased via decoupling matrix and the ABS is controlled with a robust diagonal controller. In fact, a decentralized control technique is used for our ABS control mechanism. At the second step, we exploit a multivariable technique in linear control to attack the problem. This is the Designed Linear Control with Multivariable Technique. The Optimal Eigenstructure assignment with the Genetic Algorithm (GA) method is also applied. Simulation and comparison studies are used to show the effectiveness of the proposed methods.

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In this paper, a new methodology for robust controller design in nonlinear multivariable systems is suggested to guarantee asymptotic output tracking. The systems under consideration are perturbed by functionally bounded matched and unmatched uncertainties/perturbations and assumed to be described in the strict-feedback form. The main idea of the methodology is based on the combination of conventional sliding mode control and backstepping algorithm. The proposed controller called nested sliding mode controller that is obtained through a stepwise algorithm. It has the ability of rejecting nonvanishing perturbations by using dynamic switches, unlike conventional and other hierarchical sliding mode design methods. Performance is studied through theorems and verified by two numerical examples.

In this article, a new methodology for robust actuator weighting in the control allocation (CA) problem of input redundant feedback systems is addressed. The methodology is based on the control structural properties of the plant which were previously used for control configuration selection. Robust performance (RP) measures including H ∞ norm and structured singular value of the closed-loop system are used in this article. The capability of the approach is proven with application to lateral dynamics control of the vehicle over-actuated with front and rear steering systems. Employing the RP measures, it is concluded that the vehicle feedback control with front steering angles gives superior RP properties in comparison with the feedback loop of the rear steering angles. Based on these results, the penalty weightings in the cost function of the CA unit are determined. Simulation results based on nonlinear seven degrees of freedom vehicle handling model show that the selection of penalty weightings in the CA unit based on the RP properties of the control inputs (front and rear steering angles) improves the RP of the closed-loop.

In this paper, sliding mode control is utilized for stabilization of a particular class of nonlinear polytopic differential inclusion systems with fractional-order-0 < q < 1. This class of fractional order differential inclusion systems is used to model physical chaotic fractional order Chen and Lu systems. By defining a sliding surface with fractional integral formula, exploiting the concept of the state space norm, and obtaining sufficient conditions for stability of the sliding surface, a special feedback law is presented which enables the system states to reach the sliding surface and consequently creates a sliding mode control. Finally, simulation results are used to illustrate the effectiveness of the proposed method.

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 note, an adaptive observer is considered for simultaneous estimation of the states and unknown parameters of linear stationary systems with faulty measurements. Since entries of system matrices are functions of only a few parameters, it is enough to tune those parameters, instead of adapting all entries. This leads to reduced adaptation laws. Moreover, the problem of measurement offset and gain faults are also considered. The stability and convergence of the proposed adaptive observer is investigated. Simulation results validate the performance of the proposed adaptive observer.

In this paper the use of proportional-integralderivative (PID) switching controllers is proposed for the control of a magnetically actuated mass-spring-damper system which is composed of two masses M1 and M2; each mass is jointed to its own spring. Two different modes occur during the system motion; a PID controller is designed for each mode and a switching logic is applied in order to recognize the system's position to switch to the proper controller. Finally, simulation results are employed to show the performance of the proposed switched PID controller. Also, comparison results with the previously used model predictive controller (MPC) are provided.

Switching control is employed in many adaptive control strategies to overcome difficulties encountered in the control design problems that cannot be routinely solved by conventional robust and adaptive control architectures. A key stage in switching control design is the switching logic. This paper proposes a new switching scheme based on the control performance index (CPI) concepts. The performance assessment index is primarily calculated using the Markov parameters of the closed loop transfer function to assess the closed loop performance of the regulatory and tracking control systems. It is shown that employing CPI can lead to proper switching between different controllers. Finally, simulation results are provided show the main points of the paper.

This article considers an improvement in dead zone modification scheme for robust model-reference adaptive control of SISO and TITO systems, described by input-output uncertain linear models with actuator faults. In the conventional approach, adaptation of the controller parameters is ceased in the dead zone, which leads to steady state tracking error. This problem is resolved by tuning specific controller parameters inside the dead zone. The stability of the closed loop system and tracking of step commands are verified analytically. A comparative numerical simulation is performed to illustrate the effectiveness of the proposed scheme in control of an engine-dynamometer system.

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, the stabilization of linear time-invariant systems with fractional derivatives using a limited number of available state feedback gains, none of which is individually capable of system stabilization, is studied. In order to solve this problem in fractional order systems, the linear matrix inequality (LMI) approach has been used for fractional order systems. A shadow integer order system for each fractional order system is defined, which has a behavior similar to the fractional order system only from the stabilization point of view. This facilitates the use of Lyapunov function and convex analysis in systems with fractional order 1

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 paper, an approach based on the variable structure control is proposed for stabilization of linear time invariant fractional order systems (LTI-FOS) using a finite number of available state feedback controls, none of which is capable of stabilizing the LTI-FOS by itself. First, a system with integer order derivatives is defined and its existence is proved, which has stability equivalent properties with respect to the fractional system. This makes it possible to use Lyapunov function and convex analysis in order to define the sliding sector and develop a variable structure control which enables the switching between available control gains and stabilizing the fractional order system.

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.

Flexibility and aeroelastic behaviors in large space structures can lead to degradation of control system stability and performance. The model reference adaptive notch filter is an effective methodology used and implemented for reducing such effects. In this approach, designing a model reference for adaptive control algorithm in a flight device such as a launch vehicle is very important. In this way, the vibrations resulting from the structure flexibility mostly affects the pitch channel, and its influences on the yaw channel are negligible. This property is used and also the symmetrical behavior of the yaw and pitch channels. In this paper, by using this property and also the symmetrical behavior of the yaw and pitch channels, a new model reference using identification on the yaw channel is proposed. This model behaves very similar to the rigid body dynamic of the pitch channel and can be used as a model reference to control the vibrational effects. Simulation results illustrated applies the proposed algorithm and considerably reduces the vibrations in the pitch channel. Moreover, the main advantage of this new method is the online tuning of the model reference against unforeseen variations in the parameters of the rigid launch vehicle, which has not been considered in the previous works. Finally, robustness of the new control system in the presence of asymmetric behavior is investigated.

Dynamic Matrix Control (DMC) is a widely used model predictive controller (MPC) in industrial plants. The successful implementation of DMC in practical applications requires a proper tuning of the controller. The available tuning procedures are mainly based on experience and empirical results. This paper develops an analytical tool for DMC tuning. It is based on the application of Analysis of Variance (ANOVA) and nonlinear regression analysis for First Order plus Dead Time (FOPDT) process models. It leads to a simple formula which involves the model parameters. The proposed method is validated via simulations as well as experimental results. A nonlinear pH neutralization model is used for the simulation studied. It is further implemented on a laboratory scale control level plant. A robustness analysis is performed based on the simulation results. Finally, comparison results are provided to show the effectiveness of the proposed methodology.

In this paper, the problem of supervisory based switching Quantitative Feedback Theory (QFT) control is proposed for the control of highly uncertain plants. According to this strategy, the uncertainty region is divided into smaller regions with a nominal model. It is assumed that a QFT controller exits for robust stability and performance of the individual uncertain sets. The proposed control architecture 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 candidate local model behavior 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 for stability reasons. It is shown that this strategy leads to improved closed loop performance, and can also handle the uncertainty sets that can not be tackled by a single QFT robust controller. Simulation results are proposed to show the effectiveness of the proposed methodology.

n this paper, an extension of the modified generalized predictive control (GPC) algorithm and a tuning strategy is presented. To take the plant dynamics such as under damped behavior and the effect of zeros into account, extension to the second order plus dead time (SOPDT) of the first order plus dead time (FOPDT) modified GPC method is proposed. It is shown that this method is computationally undemanding. Also, implementation is more straightforward than conventional GPC algorithms. Moreover, the proposed tuning strategy enables a fast implementation of the GPC with regard to nominal stability and desired performance. The simplicity of this strategy and its wide applicability makes it readily accessible to practitioners for utilization. Multiple simulation results are provided to show the effectiveness of the proposed algorithm.

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.

Multi-objective design problem is the optimization of various and often conflicting objectives for a complex system. In this paper, optimization is performed using Linear Matrix Inequalities (LMI's). A switching strategy is proposed in order to improve the multi-objective control performance. Each controller design is based on a set of performance specifications. Instead of considering all the specifications defined by respective LMI sets simultaneously, only relevant objectives are included in the control design procedure. Then, switching is performed to meet multiple objectives. Assurance of the overall stability of the closed-loop is acknowledged via specific controller realization. Multi-objective designs are prone to conservatism, which is greatly reduced by the switching approach. The efficiency of the proposed methodology is illustrated through an example.

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.

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.

This paper proposes a new method for the adaptive control of nonlinear in parameters (NLP) chaotic systems. A method based on Lagrangian of a cost function is used to identify the parameters of the system. Estimation results are used to calculate the Lyapunov exponents adaptively. Finally, the Lyapunov exponents placement method is used to assign the desired Lyapunov exponents of the closed loop system.

This paper investigates chaos control for scalar delayed chaotic systems using sliding mode control strategy. Sliding surface design is based on delayed feedback controller. It is shown that the proposed controller can achieve stability for an arbitrary unstable fixed point (UPF) or unstable periodic orbit (UPO) with arbitrary period. The chaotic system used in this study to illustrate the theoretical concepts is the well known Mackey–Glass model. Simulation results show the effectiveness of the designed nonlinear sliding mode controller.

This paper studies Internal Model Control (IMC) and its structure and applications in process. By using he capability of IMC we obtained PID coefficients and designed the IMC-PID controller. Then the IMC-PID is used in multi controller structure to control the pressure plant (RT532).

Control system design for a complex system encompasses the optimization of different and often conflicting objectives leading to a multiple objective design problem. In this paper, a switching strategy is proposed to solve the multiple objective controller design. Each controller is designed based on a set of performance specifications. Control realization considerations are used to ensure overall closed loop stability. Linear matrix inequalities are employed in the controller design process. Multi objective designs are always prone to over conservatism, which is greatly reduced by the switching strategy. Simulation results are used to show the effectiveness of the design methodology.

This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network based Fuzzy Inference System (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and forgetting factor recursive least square (FFRLS) for training the conclusion part. Two famous training algorithms for ANFIS are the gradient descent (GD) to update antecedent part parameters and using GD or recursive least square (RLS) to update conclusion part parameters. Lyapunov stability theory is used to study the stability of the proposed algorithms. This paper, also studies the stability of PSO as an optimizer in training the identifier. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data. Also, stable learning algorithms for two common methods are proposed based on Lyapunov stability theory and some constraints are obtained.

Multi-objective control is the problem of optimising various conflicting objectives. In this paper, the multi-objective active vibration control via switching is proposed. The switching is applied to the separately designed H 2 and H w controllers, instead of considering both objectives in the synthesis of a single controller. Each controller is designed using Linear Matrix Inequalities (LMIs). The overall stability of the closed-loop is guaranteed through a specific controller realisation. The H 2 controller is utilised to improve the transient response and the H w controller in steady-state performance. The switching approach in multi-objective control reduces the conservatism of the design. Control of the active vibration system as a regulator is studied in the present paper.

Bioprocesses are involved in producing different pharmaceutical products. Complicated dynamics, nonlinearity and non-stationarity make controlling them a very delicate task. The main control goal is to get a pure product with a high concentration, which commonly is achieved by regulating temperature or pH at certain levels. This paper discusses model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor. The novel approach used here is to use the inverse of penicillin concentration as a cost function instead of a common quadratic regulating one in an optimization block. The result of applying the obtained controller has been displayed and compared with the results of an auto-tuned PID controller used in previous works. Moreover, to avoid high computational cost, the nonlinear model is substituted with neuro-fuzzy piecewise linear models obtained from a method called locally linear model tree (LoLiMoT).

Current developments in the aerospace flight devices have led to a control system being designed in the presence of elastic behaviour. However, there are several ways to reduce the destructive effects of vibration in flexible systems. In this paper, a practical approach called ‘rigid model reference’ is extended to two vibration modes based on the gradient method. Furthermore, the existence of two dominant bending vibration modes in the output of measurement devices leads to a redesign of the control system. Robust stability of the new algorithm is investigated by using Kharitonov theorem. Simulation results illustrate considerable reduction of vibration effects on the output of measurement system considering the first and the second bending vibration modes.

This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network-based Fuzzy Inference System (ANFIS) as a system identifier. The proposed hybrid learning algorithm is based on the particle swarm optimization (PSO) for training the antecedent part and the extended Kalman filter (EKF) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. Comparison results of the proposed approach, PSO algorithm for training the antecedent part and recursive least squares (RLSs) or EKF algorithm for training the conclusion part, with the other classical approaches such as, gradient descent, resilient propagation, quick propagation, Levenberg–Marquardt for training the antecedent part and RLSs algorithm for training the conclusion part are provided. Moreover, it is shown that applying PSO, a powerful optimizer, to optimally train the parameters of the membership function on the antecedent part of the fuzzy rules in ANFIS system is a stable approach which results in an identifier with the best trained model. Stability constraints are obtained and different simulation results are given to validate the results. Also, the stability of Levenberg–Marquardt algorithms for ANFIS training is analyzed.

In this paper, after defining pure proportional navigation guidance in the 3-dimensional state from a new point of view, range estimation for passive homing missiles is explained. Modeling has been performed by using line of sight coordinates with a particular definition. To obtain convergent estimates of those state variables involved particularly in range channel and unavailable from IR trackers, nonlinear filters such as sequential U-D extended Kalman filter and Unscented Kalman filter in modified spherical coordinate combined with a modified proportional navigation guidance law are proposed. Simulation results indicate that the proposed tracking filters in conjunction with the dual guidance law are able to provide the convergence of the range estimate for both maneuvering and nonmaneuvering targets.

This paper introduces a new scheme for building a clean room with no unwanted sound in it. Resent efforts for building such rooms always encounter problems because of the stochastic nature of these systems. A real clean room not only faces some non-moving sources, but also faces some moving ones, that probably the sound source is a moving one in reality. In order to solve the problem of motion of those moving sources, one must design a method to solve this problem which we definitely encounter with. Active noise cancellation needs the direction of transmission of the unwanted sound in order to reduce its effect. Hence, these rooms need an identifier, which we suggest a clean room identification algorithm in this paper. Recent efforts for building these kind of room, always suppose the non-moving sound sources which we may encounter a moving one in every day life. The other problem is the mutual effect of each source on the other microphones. Those non-moving sources just cause mutual effect on all microphones and compel us using a separator. Therefore, these systems need a separator, which we use BSS algorithms in order to reach this aim. Finally, simulation results are provided to illustrate the main points.

This paper presents the adaptive control of chaotic systems, which are nonlinear in parameters (NLP). A method based on Lagrangian of an objective functional is used to identify the parameters of the system. Also this method is improved to result in better rate of convergence of the estimated parameters. Estimation results are used to calculate the Lyapunov exponents adaptively. Finally, the Lyapunov exponents placement method is used to assign the desired Lyapunov exponents of the closed loop system. Simulation results are provided to show the effectiveness of the results.

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.

Current state-of-the-art approaches for control of hybrid systems face two main important challenging problems which are stabilization and computational complexity. This paper aims at improving a special strategy i.e. predictive ontrol for a special class of hybrid systems i.e. mixed logical dynamical systems. For mixed logical dynamical ystems as a main class of hybrid systems, the only existing way to ensure the closed loop stability of predictive controllers is to use a terminal state equality constraint in the successive optimization problems. Limitations caused by this type of constraint have been discussed. Contractive predictive control is proposed as a good alternative which assures the closed loop stability in a more feasible manner. As a Lyapunov function, the L1 norm of the state vector is enforced to shrink in successive optimization steps. A suboptimal version of contractive MPC scheme has been proposed which reduces the computational complexity of the control problem while preserving the stability

In this method, firstly, the frequency responses of system in a few points are predicted and are compared with the frequency response of the model reference that is the proper loop gain function. In the next step, a second order Controller for compensating is designed. Finally, a benchmark for the convergence of the real loop to the reference function and the stability of the closed loop is introduced. As the convergence of the response is adequate in a limited band the structural information of the system such as order of the system, order and number of delays is not necessary. The application of this method is in control of high order system and the systems with delayed response.

Minimal Stopping Distance, Guaranteed Steering ability and Stability are the three most important proposes in Anti-lock Braking System(ABS)realm. The ABS system is nonlinear, time variant, multivariable and uncertain. Up to now several researches have been done on ABS control system, but nearly all of them are intricate and expensive. In this paper we exploit a multivariable technique in linear control to attack the problem, which is Designed Linear Control with Multivariable Technique. The Optimal Eigenstructure Assignment with Genetic Algorithm(GA) method is also applied. Simulation and comparison studies are used to show the effectiveness of the proposed methods.

Decentralized control structure is widely employed in many industrial multivariable processes. In this approach, control structure design and in particular input–output pairing is a vital stage in the design procedure. There are several powerful methods to select the appropriate input–output pair in linear multivariable plants. However, in the face of plant uncertainties, the input–output pairs can change. Input–output pairing problem, in the presence of uncertainties, and its consequences on the pairing problem have not been widely addressed. In this paper, Hankel interaction index array is used to choose the appropriate input–output pair and a new method is proposed to compute Hankel interaction index array, which reduces the computational load. Also, a theorem will be presented to show the effect of additive uncertainties on input–output pairing of the process. An upper bound on the element variations of Hankel interaction index array of the additive uncertainties in state space framework is given to show the possible change in input–output pairing. Finally, two typical processes are employed to show the main points of the proposed methodology.

In this paper a Neural Predictive Controller (NPC) designed to control a broad class of process systems. Neural network identification yields nonlinear global model of the unknown system. LevenbergMarquardt (L-M) optimization method is used to find optimal control signal to minimize future errors of the objective function of predictive controller. Inequality constraints of actuators are added to the objective function through a penalty term which increases drastically as it approaches the limitations. To use the controller for wide range of process systems, an initial phase runs before the main controller to determine parameters. This phase moves the system output to operating point and applies PID controller with APRBS reference signal. The gathered data are used to estimate parameters such as pure delay, prediction horizon, control coefficient and identification order. To validate the approaches, the controller has implemented in level, pressure and flow pilot plants and compared with conventional controller which shows faster and smoother tracking results.

In this paper, a model predictive control scheme for a class of nonlinear systems is presented. In the proposed algorithm, the new cost function for MPC is defined. This cost function is inspired by the structure of passivity-based control. By simple tuning of weighting matrices, the asymptotic stability is guaranteed. Moreover, a closed-form solution to the optimal control problem is calculated via representing the nonlinear system in the state-dependent coefficient form of the state-space model. This point is of great importance in online applications. To demonstrate its efficiency, the passivity-based structured MPC is applied to control a rotational motion of a rigid body.

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.

In this paper, a new stabilizing control law with respect to a control Lyapunov function (CLF) is presented. This control law is similar to the pointwise min-norm control law. This control law is designed to maximize the angle between the gradient of the control Lyapunov function and the time derivative of the state vector at the state trajectory, which is defined in what follows as the “pointwise maximum angle control law.” A comparison with the pointwise minnorm control law is provided. A criterion of the stability performance of control laws that are designed with respect to a CLF is presented. Also, by proposing the concept of the “eigen-angle” for real square nonsingular matrices, the stabilization of some nonaffine nonlinear systems, and the construction of a CLF for such systems are reduced to the construction of CLFs for affine nonlinear (linear) systems. Finally, simulation results are provided to show the effectiveness of the proposed methodologies.

In this correspondence paper, a theorem is given based on the main results of Kariwala et al. 1 for input-output pairing analysis for uncertain multivariable systems. A method to compute the relative gains' variation bound of RGA to inputoutput pairing analysis is provided. The results can decrease the computational load in large-scale uncertain systems, solve the sensitivity analysis problem, and propose the appropriate pair, when there is no sign change for relative gains.

Decentralized control is a well established approach to control the multivariable processes. In this approach, control structure design and in particular input-output pairing is a vital stage in the design procedure. There are several powerful methods to select the appropriate input-output pair in linear multivariable systems. However, despite the fact that most practical processes are nonlinear, there is no general method to select the appropriate input-output pair for nonlinear multivariable systems. In this study, a new general approach to input-output pairing for linear and nonlinear multivariable systems is proposed. Simulation results are employed to show the effectiveness of the proposed methodology.

Decentralised control is widely used for the control of multivariable plants. Prior to the design of the decentralised controllers, input-output pairing is an important step in the design procedure. In the face of unknown, uncertain or time varying plant parameters, the input-output selection may endure fundamental changes, which will severely degrade the decentralised controller performance. This paper proposes a reconfigurable structure for the design of the decentralised controller based on the adaptive control strategies. Simulation results are provided to show the effectiveness of the proposed methodology.

In this paper, a novel Two-Degree-Of-Freedom (2DOF) design procedure for Multi-Input Multi-Output Quantitative Feedback Theory (MIMO QFT) problems with Tracking Error Specifications (TESs) is presented. In the proposed procedure, the feedback compensator design is separated from the pre-filter design, using the model matching approach and the unstructured uncertainty modeling concept. This paper specially deals with an appropriate transformation of the MIMO system to the equivalent SISO problems, which allows easy design. Simulation results have been provided to show the effectiveness of the proposed methodology.

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

A gradient based approach for the design of set-point tracking adaptive controllers for nonlinear chaotic systems is presented. In this approach, Lyapunov exponents are used to select the controller gain. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, an effective adaptive strategy is used for online identification of Lyapunov exponents and adaptive control of nonlinear chaotic plants. Also, a nonlinear observer for estimation of the states is proposed. Simulation results are provided to show the effectiveness of the proposed methodology.

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).

Hybrid systems theory as a growing field in control theory provides some contributions for traditional control problems. Control of switched linear systems as a member of these categories has well-known solutions like multiple model control. Multiple model controllers provide a global control action by interpolating the individually-designed controllers. Since the hybrid systems methods are usually more complicated than the conventional control schemes, it is of great importance to explain these potential superiorities. The goal of this paper is to explain potential benefits which could be achieved by using hybrid control methods. Predictive control - as a powerful strategy to deal with complicated dynamics - is selected as the design basis for hybrid controller and for a multiple model controller. An illustrative test bench problem is introduced to compare the behavior of two controllers. It has been shown that the hybrid controller provides more intelligent and systematic design procedure for control of switched linear systems and is superior to multiple model controller in the sense of speed of response, optimality and domain of attraction

In this paper, generalized predictive control (GPC) algorithm is implemented to control an Earth station antenna. Nonlinear term in motors caused by coulomb friction is modeled by a dead zone block. Simulation results show the effectiveness of GPC method for robust control in the presence of dead zone nonlinearity without a priori knowledge about upper and lower bounds of dead zone

In this paper, we propose a one-layered neural network that recovers its input variables by genetic algorithms to solve the systems of linear equations (or, equivalently, matrix inversion). First we described solving systems of linear equations (matrix inversion) by mentioned neural network. Then, experimental results are presented to show the effectiveness of the approach. Finally, future avenue of this research is proposed.

This paper describes the implementation of picture stabilizer in 3-degree of freedom table in image stabilizer. There are two innovative aspects of this work. First, parameter estimation is used to adapt the feedforward compensation terms instead of the gains of the feedback controller, as usually is the case in conventional indirect self-tuning regulators. Second, the complete adaptive controller has been implemented with C program and PCL812 card and encoder card and motor driver for command the motors. In result one method with hybrid increase accuracy system, specially when input error signal is large and need to maximum speed control system. In this system frequency of 0.3 Hz , thus we use gyro for estimate of table position. In this paper, it is specifically implemented and demonstrated on a gyro mirror line-of sight (LOS) system

A model-based approach to adaptive control of chaos in non-linear chaotic discrete time systems is presented. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, an effective adaptive strategy is proposed for on-line identification of Lyapunov exponents. The control aim is that the plant output changes in accordance with the output of the linear desired model. Simulation results are provided to show the effectiveness of the proposed methodology.

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Quantitative design of robust control systems proposes a transparent and practical controller design methodology for uncertain plants. In the case of large plant uncertainties, the resulted robust controller would be unnecessarily high order with large bandwidth. On the other hand, adaptive controllers can also tackle the control of unknown uncertain plants. However, the controller would be nonlinear and time varying. In this paper, a combined design methodology based on quantitative feedback theory (QFT) and externally excited adaptive system (EEAS) is proposed. This controller can handle large plant parameter uncertainties with lower bandwidth. Also, a random optimization technique is employed to optimally design the overall robust adaptive controller. Simulation results are used to show the effectiveness of the proposed design methodology.

This paper proposes a reconfigurable controller design method for multivariable systems, which is capable of dealing with order-change problems that may occur in an after-fault system. A new method is proposed to recover the nominal closed-loop performance after a fault occurrence in the system. This approach uses the eigenstructure assignment. Unlike the previously developed approaches, the new method can be implemented in the case when the fault leads to order change of the after-fault model. Also, it can be used to solve the problems in which the set of after-fault open-loop and closed-loop eigenvalues have common elements, especially when the system becomes uncontrollable or unobservable due to the fault. The method guarantees the stability of the reconfigured closed-loop system in the presence of output feedback. Finally, simulation results are provided to show the effectiveness of the proposed method for an aircraft model.

New approaches to design static and dynamical reconfigurable control systems are proposed based on the eigenstructure assignment techniques. The methods can recover the nominal closed-loop performance after a fault occurrence in the system, in the state and output feedback designs. These methods are capable of dealing with order-reduction problems that may occur in an after-fault system. The problem of robust reconfigurable controller design, which makes the after-fault closed-loop system insensitive as much as possible, to the parameter uncertainties of the after-fault model is considered. Steady state response of the after-fault system under the unit step input is recovered by the means of a reconfigurable feed-forward compensator. The methods guarantee the stability of the reconfigured closed-loop system in the case of output feedback. For the faulty situations, in which the order of the pre-fault and after-fault closed-loop systems are the same, sufficient regional pole assignment conditions for the reconfigured system are derived. Finally, simulation results are provided to show the effectiveness of the proposed methods for two aircraft models.

This paper considers the adaptive computation of Lyapunov Exponents (LEs) from time series observations based on the Jacobian approach. It is shown that the LEs can be calculated adaptively in the face of parameter variations of the dynamical system. This is achieved by formulating the regression vector properly and adaptively updating the parameter vector using the Recursive Least-Squares principles. In cases where the structure of the dynamical system is unknown, a general non-linear regression vector for local model fitting based on a locally adaptive algorithm is presented. In this case, the Recursive Least-Squares method is used to fit a suitable local model, then by state space realization in canonical form, the Jacobian matrices are computed which are used in the QR factorization method to calculate the LEs. This method essentially relies on recursive model estimation based on output data. Hence, this on-line dynamical modeling of the process will circumvent the computations typically required in the reconstructed state space. Therefore, difficulties such as the problem of large number of data and high computational effort and time are avoided. Finally, simulation results are presented for some well-known and practical chaotic systems with time varying parameters to show the effectiveness of the proposed adaptive methodology.

In this paper, a method for estimating an attractor embedding dimension based on polynomial models and its application in investigating the dimension of Bremen climatic dynamics are presented. The attractor embedding dimension provides the primary knowledge for analyzing the invariant characteristics of the attractor and determines the number of necessary variables to model the dynamics. Therefore, the optimality of this dimension has an important role in computational efforts, analysis of the Lyapunov exponents, and efficiency of modeling and prediction. The smoothness property of the reconstructed map implies that, there is no self-intersection in the reconstructed attractor. The method of this paper relies on testing this property by locally fitting a general polynomial autoregressive model to the given data and evaluating the normalized one step ahead prediction error. The corresponding algorithms are developed in uni/multivariate form and some probable advantages of using information from other time series are discussed. The effectiveness of the proposed method is shown by simulation results of its application to some well-known chaotic benchmark systems. Finally, the proposed methodology is applied to two major dynamic components of the climate data of the Bremen city to estimate the related minimum attractor embedding dimension.

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 a new method for decentralized stabilization of a large-scale system in general form via state-feedback is presented. An appropriate descriptor system is defined for a large-scale system, such that the new system is in input-decentralized form. The interactions between the subsystems are considered as uncertainty. Sufficient conditions for stability of the closed-loop uncertain system are introduced. By appropriately assigning the eigenstructure of each isolated subsystem, these conditions are satisfied. This is accomplished by using the method suggested by Patton and Liu, such that the effects of the interconnections between the subsystems are compensated via the combination of genetic algorithms and gradient-based optimization.

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.

The problem of embedding dimension estimation from chaotic time series based on polynomial models is considered. The optimality of embedding dimension has an important role in computational efforts, Lyapunov exponents analysis, and efficiency of prediction. The method of this paper is based on the fact that the reconstructed dynamics of an attractor should be a smooth map, i.e. with no self intersection in the reconstructed attractor. To check this property, a local general polynomial autoregressive model is fitted to the given data and a canonical state space realization is considered. Then, the normalized one step ahead prediction error for different orders and various degrees of nonlinearity in polynomials is evaluated. This procedure is also extended to a multivariate form to include information from other time series and resolve the shortcomings of the univariate case. Besides the estimation of the embedding dimension, a predictive model is obtained which can be used for prediction and estimation of the Lyapunov exponents. To show the effectiveness of the proposed method, simulation results are provided which present its application to some well-known chaotic benchmark systems.

The input-output pairing of multivariable plants with parametric uncertainty can vary in the face of large plant parameter variations. The Relative Gain Array (RGA) analysis is a powerful tool for the input-output pairing of linear multivariable plants. In the case of parametric uncertainties, RGA elements may vary accordingly. Hence, a test is proposed to identify the change in the input-output pairing in the presence of parametric uncertainties.

In this paper, the problem of achieving robust stability for linear large-scale systems by decentralized feedback is considered. Sufficient conditions for stability of closed-loop system are introduced. By appropriately assigning the eigenstructure of each isolated subsystem via output feedback or state feedback, these conditions are satisfied. Based on the eigenstructure assignment result and the matrix eigenvalue sensitivity theory, a method for decentralized robust stabilization is presented.

In this paper a new method for robust decentralised control of large-scale systems using quantitative feedback theory (QFT) is suggested. For a given large-scale system an equivalent descriptor system is defined. Using this representation, closed-loop diagonal dominance sufficient conditions over the uncertainty space are derived. It is shown by appropriately choosing output disturbance rejection model in designing QFT controller for each isolated subsystem, these conditions are achieved. Then a single-loop quantitative feedback design scheme is applied to solve the resulting series of individual loops to guarantee the satisfaction of predefined MIMO quantitative specifications.

This paper considers the problem of achieving stability and desired dynamical transient behavior for linear large-scale systems, by decentralized control. It can be done by making the effects of the interconnections between the subsystems arbitrarily small. Sufficient conditions for stability and diagonal dominance of the closed-loop system are introduced. These conditions are in terms of decentralized subsystems and directly make a constructive H∞ control design possible. A mixed H∞ pole region placement is suggested, such that by assigning the closed-loop eigenvalues of the isolated subsystems appropriately, the eigenvalues of the overall closed-loop system are assigned in desirable range. The designs are illustrated by an example.

New necessary and sufficient conditions for multivariable pole placement (MVPP) and entire eigenstructure assignment (EEA) through static linear multivariable output feedback are established. It is shown that the resultant matrix is of full rank and all design freedoms are retained. The problem of static linear multivariable output feedback control law design is then defined. Based on the EEA concept and sufficiency of the regional pole placement, the design is (re)formulated in terms of a constrained nonlinear optimization problem. To this end, some decoupling indices for noninteractive performance are defined, their necessary and sufficient conditions are derived and tracker design is addressed. The problem formulation well suits the application of random/intelligent optimization techniques. By way of this approach, optimal robust stability/performance, noninteractive performance, reliability, actuator limitations and low sensitivity in the face of structured or unstructured plant uncertainties are achieved. The effectiveness of the proposed methodology is demonstrated by simulation results using genetic algorithm.

The problem of achieving stability and certain H/sub /spl infin// performance objective for a large-scale system by a decentralized feedback law is considered. It is shown in order to reduce the sensitivity to the interactions, the states of the other subsystems can be considered as external disturbances for each subsystem. An appropriate H/sub /spl infin// controller is designed for each subsystem. Solving H/sub /spl infin// problems for the subsystems, the sensitivity to the interactions is reduced and the performance problem which is formulated as the standard weighted mixed sensitivity H/sub /spl infin// problem, is solved. Sufficient conditions are derived when satisfied to assure the overall stability.

Quantitative design of robust control systems proposes a transparent and practical controller design methodology for uncertain single-input single-output and multivariable plants. There are several steps involved in the design of such controllers. The main steps involved in the design are template generation, loop shaping and pre-filter design. In the case of multivariable uncertain plants, manipulation of tolerance bounds within the available freedom, for both sequential and non-sequential designs, consideration of template size of next step in sequential design, and the appropriate selection of the nominal transfer function matrices in the equivalent disturbance attenuation design are also crucial steps. In all the quantitative designs, a time-consuming trial-and-error procedure is adapted and a successful compromise between various design requirements is very much dependent on the designer experience and expertise. In this paper, these steps are reformulated in terms of different cost functions, and it is shown that the optimization of these cost functions leads to an optimal design of quantitative controllers, for both single input single output and multivariable plants. This proposes a nonlinear constrained optimization problem that can be easily solved using any of the random optimization techniques. Simulation results are used to show the effectiveness of the proposed method.

Singular perturbation methods are used to demonstrate that the step-response matrices of linear multivariable systems containing small ‘parasitic” elements have a distinctive structure which guarantees the robustness of both non-adaptive and adaptive controllers for such systems incorporating step-response matrices. The significance of these results in relation to the modelling of multivariable plants with ‘fast” actuators and sensors is illustrated, and their validity is demonstrated by considering a typical gas-turbine jet engine.

Since many industrial processes are essentially linear multivariable type-one plants (i.e. linear multivariable plants with unbounded step-response matrices but with bounded impulse-response matrices), the methodologies of Porter and Jones (1986) for linear multivariable type-zero plants are extended to embrace such linear multivariable type-one plants. It is shown that the proportional and derivative controller matrices in the resulting PD controllers can be directly determined from open-loop impulse-response tests performed on linear multivariable type-one plants. The disturbance-rejection properties of these controllers are fully developed by modifying the digital PD controller by the inclusion of an outer PID loop. The robustness propertcs of these PD-PID controllers are assessed by characterizing, in terms of the steady-state impulse-response matrices of nominal and actual plants, the admissible plant perturbations that can be tolerated. The effectiveness of this design methodology is illustrated by designing a tunable digital set-point tracking PD-PID controller for a steel mill.

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.

It is shown that, by incorporating on-line recursive identifiers to provide updated steady-state plant transfer function matrices for inclusion in digital proportional-plus-integral control laws, highly robust adaptive digital set-point tracking PI controllers can be readily designed for nonminiraum-phase multivariable plants. The effectiveness of this methodology in the absence of precise a priori information concerning plant order is illustrated by designing an adaptive digital set-point tracking PI controller for a distillation column with nonminimum-phase characteristics using both exactly parametrised and grossly underparametrised models.