Search

This note deals with the problem of controlling an uncertain multivariable plant in the presence of input saturation via switching among a finite family of controllers having a generalized anti-windup architecture. The problem is addressed within the multi-model unfalsified adaptive switching control framework. It is shown that proper definitions of fictitious references and test functionals allow to prove stability of the overall switching scheme, provided that at least one controller in the finite family is stabilizing. The satisfiability of this assumption is discussed and simulation results are reported.

This paper investigates the problem of decentralized model reference adaptive control (MRAC) for a class of stable large scale interconnected systems. The interconnections are nonlinear with time varying delays which are bounded by polynomials with unknown gains. Also, it is assumed that both state and input delays are present. To overcome the input delay effect, a nested predictor based approach is adopted to predict the future states. The uniformly bounded stability of the closed loop system is proved by employing a suitable Lyapunov function. The effectiveness of the proposed approaches is illustrated by a numerical example.

Summary Fault-tolerant control systems are vital in many industrial systems. Actuator redundancy is employed in advanced control strategies to increase system maneuverability, flexibility, safety, and fault tolerability. Management of control signals among redundant actuators is the task of control allocation algorithms. Simplicity, accuracy and low computational cost are key issues in control allocation implementations. In this paper, an adaptive control allocation method based on the pseudo inverse along the null space of the control matrix (PAN) is introduced in order to adaptively tolerate actuator faults. The proposed method solves the control allocation problem with an exact solution and optimized l∞ norm of the control signal. This method also handles input limitations and is computationally efficient. Simulation results are used to show the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

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.

In this paper a novel method for adaptive predictive control of a launch vehicle is presented. Nonlinear dynamics of these systems cause challenging problems in controller design. Linearizing the system in diverse operating points and designing appropriate controllers for these systems is an interesting idea in industry. The outcome is a linear time varying (LTV) system. Dealing with time varying dynamics is a challenging issue in control theory. Adaptive control approach presents a well-established methodology to address the subject of flight control systems. This paper proposes an indirect adaptive predictive idea to control the pitch channel dynamics of a launch vehicle. For this purpose, a robust estimator and a robustly-tuned generalized predictive controller are incorporated to present a robust adaptive scheme. The proposed technique is applied to pitch channel model of Vanguard missile. A set of test scenarios is conducted to explore the performance of proposed controller in various conditions. The results demonstrate the fidelity of this method to yield high performance in the presence of time-varying parameters under various un-modeled dynamics and external disturbances.

In this paper, Linear Quadratic Gaussian (LQG) controller extended to a class of nonlinear systems based on subspace matrices using bilinear model. LQG controller design based on subspace matrices provides directly from system input output data. Therefore it is more useful for systems that their models are not available. Since the most practical systems are nonlinear, LQG controller design based on linear subspace model is reflected to a weak control performance or even instability. To overcome this problem, LQG controller design based on bilinear subspace model is presented. Simulation results and comparison studies are provided to show the effectiveness of 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 ana- lytical 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 effective- ness of the proposed tuning strategy.

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.

In this paper, a new practical robust water level control system for the U-tube steam generator (UTSG) using the quantitative feedback theory (QFT) is proposed. The steam generator is a nonlinear uncertain plant. However, the steam generator behaves as a linear uncertain and nonminimum phase plant at its different operating points, which makes its control a challenging problem. The control problem is to design controllers such that the closed-loop plant satisfies the robust stability, disturbance rejection, and robust tracking specifications that are derived from a desired steam generator performance. In the QFT design methodology, these specifications are satisfied by generating the plant templates, the composite bounds, and a nominal plant loop shaping procedure to satisfy these bounds. Simulation results reveal that the designed QFT water level controllers will ensure all the designers’ closed-loop specifications. Also, comparison results are provided that show the effectiveness of the robust Q...

Model predictive control (MPC) is an effective control strategy in the presence of system constraints. The successful implementation of MPC in practical applications requires appropriate tuning of the controller parameters. An analytical tuning strategy for MPC of first-order plus dead time (FOPDT) systems is presented when the constraints are inactive. The available tuning methods are generally based on the user's experience and experimental results. Some tuning methods lead to a complex optimisation problem that provides numerical results for the controller parameters. On the other hand, many industrial plants can be effectively described by FOPDT models, and this model is therefore used to derive analytical results for the MPC tuning in a pole placement framework. Then, the issues of closed-loop stability and possible achievable performance are addressed. In the case of no active constraints, it is shown that for the FOPDT models, control horizons subsequent to two do not improve the achievable performance and control horizon of two provides the maximum achievable performance. Then, MPC tuning for higher order plants approximated by FOPDT models is considered. Finally, simulation results are employed to show the effectiveness of the proposed tuning formulas.

In the presence of plant uncertainties, utilizing an appropriate controller for a smooth output tracking and elimination of high-frequency disturbances, especially in accurate systems is very important. In this paper, a controller is proposed based on the robust and optimal theory to achieve a combination of such characteristics in the face of model parameter variations and unknown disturbances. The proposed controller has been simulated on a three-axis gyro-stabilized MIMO platform and comparison results with a NLPID controller simulation are provided.

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 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.

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.

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 ...

This paper presents a novel teleoperation controller for a nonlinear master–slave robotic system with constant time delay in communication channel. The proposed controller enables the teleoperation system to compensate human and environmental disturbances, while achieving master and slave position coordination in both free motion and contact situation. The current work basically extends the passivity based architecture upon the earlier work of Lee and Spong (2006) [14] to improve position tracking and consequently transparency in the face of disturbances and environmental contacts. The proposed controller employs a PID controller in each side to overcome some limitations of a PD controller and guarantee an improved performance. Moreover, by using Fourier transform and Parseval’s identity in the frequency domain, we demonstrate that this new PID controller preserves the passivity of the system. Simulation and semi-experimental results show that the PID controller tracking performance is superior to that of the PD controller tracking performance in slave/environmental contacts.

In this paper, a robust water level control system for the horizontal steam generator (SG) using the quantitative feedback theory (QFT) method is presented. To design a robust QFT controller for the nonlinear uncertain SG, control oriented linear models are identified. Then, the nonlinear system is modeled as an uncertain linear time invariant (LTI) system. The robust designed controller is applied to the nonlinear plant model. This nonlinear model is based on a locally linear neuro-fuzzy (LLNF) model. This model is trained using the locally linear model tree (LOLIMOT) algorithm. Finally, simulation results are employed to show the effectiveness of the designed QFT level controller. It is shown that it will ensure the entire designer’s water level closed loop specifications.

This paper proposes a novel approach for bilateral teleoperation systems with a multi degrees-of-freedom (DOF) nonlinear robotic system on the master and slave side with constant time delay in a communication channel. We extend the passivity based architecture to improve position and force tracking and consequently transparency in the face of offset in initial conditions, environmental contacts and unknown parameters such as friction coefficients. The proposed controller employs a stable neural network on each side to approximate unknown nonlinear functions in the robot dynamics, thereby overcoming some limitations of conventional controllers such as PD or adaptive controllers and guaranteeing good tracking performance. Moreover, we show that this new neural network controller preserves the control passivity of the system. Simulation results show that NN controller tracking performance is superior to that of conventional controllers.

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.

The reliability of an intelligent self tuning controller called the brain emotional learning based intelligent controller (BELBIC) to attitude control of a nonlinear launch vehicle (LV) simulation with hardware-in-the loop simulation (HILS) is studied. To set up the HIL system of the LV a six-degree of freedom simulation of the LV and a hydraulic actuator, which was used for the pitch channel thrust vector control (TVC) actuator of the LV, is performed. The results of the BELBIC controller with a fuzzy controller (FC) and a PID controller in this HILS of the LV to control the pitch channel of the LV have been compared.

In this paper we address the pursuing or target tracking problem where an autonomous robotic vehicle is required to move towards a maneuvering target using range-only measurements. A new switched based control strategy is proposed to solve the pursuing problem that can be described as comprising a continuous cycle of two distinct phases: i) the determination of the bearing, and ii) following the direction computed in the previous step while the range is decreasing. We provide conditions under which the switched closed-loop system achieves convergence of the relative distance error to a small neighborhood around zero. Simulation results are presented and discussed.

In this paper, the stabilization of a particular class of multi-input linear systems of fractional order differential inclusions with state delay using variable structure control is considered. First, the sliding surface with a fractional order integral formula is defined, and then the sufficient conditions for stability of the sliding surface are derived. Also, the concepts related to sliding control stabilization of differential inclusion systems with integer order are extended to differential inclusion systems with fractional order 0

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.

Neural Network Model Predictive Control (NN-MPC) combines reliable prediction of neural network with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. It is shown that this structure is prone to steady-state error when external disturbances enter or actual system varies from its model. In this paper, these model uncertainties are taken into account using a disturbance model with iterative learning which adaptively change the learning rate to treat gradual effect of the model mismatch differently from the drastic changes of external disturbance. Then, a high-pass filter on error signal is designed to distinguish disturbances from model mismatches. Practical implementation results as well as simulation results demonstrate good performance of the proposed control method.

This paper presents a robust adaptive control design methodology for multi-input multi-output (MIMO) plants based on Quantitative Feedback Theory (QFT) and Externally Excited Adaptive System (EEAS), both of which are the novel ideas of Horowitz. Self Oscillating Adaptive Systems (SOAS) are proposed to mainly overcome the problem of large gain variations, which is important in certain applications. To further improve the SOAS design, the idea of EEAS was developed. Finally, combined QFT and EEAS proposed a robust adaptive controller for SISO uncertain plants. However, due to the complex design nature of the proposed combined methodology and the difficulty of an optimal design, this line of Horowitz's research was not followed further. In this paper, to overcome the above mentioned problems the design procedure is reformulated as a set of cost functions and constraints. Genetic Algorithms are then used to solve the optimal design. Also, QFT/EEAS design is extended to multivariable uncertain plants. Sufficient conditions are derived to assure the achievement of given off-diagonal performance. Then, the given main channel performance could be achieved by using SISO QFT/EEAS method. Simulation studies indicate the effective performance of the proposed QFT/EEAS MIMO design methodology. It is shown that the proposed approach can handle large plant parameter uncertainties with lower loop bandwidths.

An improved design procedure for multi-input/multi-output (MIMO) quantitative feedback theory (QFT) problems involving tracking error specifications (TESs) has been presented. Appropriate transformation of the MIMO system to a series of equivalent single-input/single-output (SISO) problems is presented that motivates an improved synthesis procedure using feedback compensator and pre-filter transfer function matrices (TFMs). The key features of the procedure are that, for each equivalent SISO problem, (i) interactions and the effects of uncertainty are treated as an output disturbance, and (ii) sufficient conditions can be determined that assure desired levels of robust performance within the bandwidth region at a transformation cost that can be computed a priori. This paper also considers how the individual elements of the pre-filter TFM can be designed for MIMO QFT problems with a reduced level of conservatism and over-design using existing SISO methods. A benchmark quadruple-tank process is considered to illustrate the benefits of the new design paradigm.

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

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

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.

Advanced high side voltage control (HSVC) regulation presents an attractive proposition for power system control. By proper tuning of its parameters, it can improve the voltage profile of the system. In this paper, we show how it can also enhance the loadability of a multimachine system. The genetic algorithm (GA) is employed to tune the parameters. Two test systems, a 21 bus and the IEEE 118 bus, are used to check the capability of the proposed algorithm.

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 r...

In this article a high-gain decentralized controller is designed for a large-scale system. The effects of the interactions between the subsystems are considered as uncertainty for the large-scale system. A bound on the high-gain factor is computed to nullify the effects of the interactions and also to ensure the overall closed-loop stability. In order to avoid saturation, the anti-windup integrator method is used in designing high-gain controller. Due to high-gain feedback, the closed-loop system is robust with respect to output disturbances and uncertainties.

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...

A predictive fuzzy controller is designed and implemented for an industrial furnace. The furnace temperature is controlled so as to track the reference profiles accurately, and to reject the disturbances. A RLS on-line predictor is used to predict future values of the plant’s output. Using these predicted values, the future error values with respect to the reference profiles are evaluated. With regard to these errors, the fuzzy controller inferences the input power to be delivered to the furnace in order to eliminate the future tracking error.

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.

A B S T R A C T An integrated robust optimal control is presented to enhance the closed loop performance in the presence of disturbance and uncertainties, to ensure smooth tracking and elimination of high frequency disturbances especially in accurate systems with minimum power consumption. Simulation result of the proposed controller based on the combination of 2 and ! controllers is used to show the effectiveness of the proposed methodology. A 3 axis gyro-stabilized MIMO platform is considered and the results of the NLPID and a single ! controller are compared with the proposed !/ 2 controller.

Nonlinear behavior and disturbance sensitivity of the pH processes causes them to be known as an appropriate test bench for advanced controllers. Because of special behavior and varying parameters of pH processes, Multiple Model Predictive Controllers (MMPC) outperform other controllers from both regulation and disturbance rejection points of views. Two new supervisory methods based on prediction error and fuzzy weighting for MMPC are presented. Better regulation in special condition and most excellent disturbance rejection in comparison to other MMPC methods are achieved.

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

With respect to weight, energy consumption, and cost constraints, hydro-active suspension system is a suitable choice for improving vehicle ride comfort while keeping its handling. The aim of sensors selection is determining number, location, and type of sensors, which are the best for control purposes. Selection of sensors is related to the selection of measured variables (outputs). Outputs selection may limit performance and also affect reliability and complexity of control systems. In the meanwhile, hardware, implementation, maintenance, and repairing costs can be affected by this issue. In this study, systematic methods for selecting the viable outputs for hydro-active suspension system of a passenger car are implemented. Having joint robust stability and nominal performance of the closed loop is the main idea in this selection. In addition, it is very important to use these methods as a complementation for system physical insights, not supersedes. So, in the first place the system is described and the main ideas in ride comfort control are addressed. An 8 degrees of freedom model of vehicle with passive suspension system is derived and validated. Both linear and nonlinear models of the car which is equipped with hydro-active subsystem are derived. After selecting the outputs, for benefiting from minimum loop interactions, the control configuration is systematically determined. The main goal of selecting control configuration is assessing the possibility of achieving a decentralized control configuration. Finally, the system behavior is controlled by a decentralized proportional–integral–differential (PID) controller. The results indicate the efficiency of the controlled hydro-active suspension system in comparison with the passive system.

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.

This paper proposed a novel frame work for bilateral teleoperation systems with a multi-degree-of-freedom (DOF) nonlinear robotic system in master and slave side with constant time delay in communication channel. We actually extend the passivity based architecture upon the earlier work of [6] to improve position tracking and consequently transparency in the face of environmental contacts. The proposed controller employs a PID controller in each side to overcome some limitation of PD controller and guarantee good performance. Besides, we show that this new PID controller preserve the control passivity of the system. Simulation and experimental results show that PID controller tracking performance is better than PD controller tracking performance in slave/ environment contacts.

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.

This paper provides a Quantitative Feedback Theory (QFT) robust control design of a gas turbine in the presence of uncertain parameters. Frequency domain analysis, disturbance rejection properties for SISO and MIMO plants, are among the distinctive features of QFT. In this paper, a QFT robust controller satisfying the required performance despite uncertainties and various constraints on the control effort and process is designed. The nonlinear gas turbine simulator employed in this paper is based on the gas turbine thermodynamic characteristics presented within MATLAB-SIMULINK. The accuracy of this simulator has been examined through several tests by real gas turbine responses.Copyright © 2011 by ASME

Based on the Gas Path Analysis (GPA) method, nonlinear estimation and fuzzy classification theories, a comprehensive fault diagnosis system has been developed for an industrial Gas Turbine (GT). The hybrid method consists of two parts, in the first part noisy sensor output changes are translated to changes in the health parameters using an Extended Kalman Filter (EKF). In the second part the outputs of the EKF are used as the inputs of a fuzzy system. This system can isolate and evaluate the physical faults based on the predetermined rules obtained mostly from experimental data and aerothermodynamical simulations. The ratios of changes in different health parameters due to different faults and also the areas in the compressor most affected by these faults are the key factors for developing the rules. The Fuzzy Inference System (FIS) gives the fault locations in the compressor or turbine. Also, operator-friendly suggestions for the time of the compressor washing or components repair are provided. This leads to a hybrid fault detection and isolation solution for the GT, and with pre-filtering the data before use as input of fuzzy inference system, the accuracy of the fault diagnosis system is improved. Nonlinear simulation, estimation and classification results are provided to show the effectiveness of the proposed methodology.Copyright © 2011 by ASME

Inherent pH process nonlinearity and time-varying characteristics impose a highly challenging control problem. This paper presents an incorporation of offline process model identification and a QFT control methodology to develop a robust control scheme for a pH neutralization process plant on the basis of SISO QFT bounds. The obtained simulation results indicate the efficiency of the proposed control scheme to accomplish both the regulatory and servo tracking objectives.

A nonlinear model predictive control (NMPC) algorithm based on a neural network model is proposed for multivariable nonlinear systems. A multi-input multi-output model is developed using multilayer perceptron (MLP) neural network which is trained by Levenberg-Marquardt algorithm and amplitude modulated pseudo random binary (APRBS) signals with noise as data sets. Model predictive control also uses Levenberg-Marquardt algorithm for the control signal optimization. The control performance is improved by using a disturbance model that compensates both model mismatch and external disturbance. The learning rate of disturbance estimation network changes adaptively to treat the model mismatch differently from the external disturbance. Simulation results using the quadruple-tank are employed to show the effectiveness of the method.