Fuzzy Controller Design for Nonlinear Systems

In this article is studied problem of Fuzzy Controller Design For Nonlinear Systems With Case Study Of TORA System. Fuzzy control for nonlinear systems is proposed on the framework from model of Takagi-Sugeno fuzzy model and PDC(paralel distributed compensation) controller. A lyapanouv-based stabilizing fuzzy control design for nonlinear systems using Takagi-Sugeno fuzzy models is applied. The stability analysis and control design problems are reduced to linear of matrix inequality (LMI) problems. So that method of fuzzy controller design are solve a set of LMI. Approach of PDC, robust and optimal controller are applied to a nonlinear control benchmark problem with case study of TORA system. The designed fuzzy controllers are yield an asymtotic stable closed-loop system. The fuzzy controller Simulation results are given to ilustrate the utility of the present fuzzy control

H∞ fuzzy control for systems with repeated scalar nonlinearities and random packet losses

Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the H∞ fuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi-Sugeno (T-S) fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as recurrent neural networks. The measurement transmission between the plant and controller is assumed to be imperfect and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to represent the phenomenon of random packet losses. Attention is focused on the analysis and design of H∞ fuzzy controllers with the same repeated scalar nonlinearities such that the closed-loop T-S fuzzy control system is stochastically stable and preserves a guaranteed H∞ performance. Sufficient conditions are obtained for the existence of admissible controllers, and the cone complementarity linearization procedure is employed to cast the controller design problem into a sequential minimization one subject to linear matrix inequalities, which can be readily solved by using standard numerical software. Two examples are given to illustrate the effectiveness of the proposed design method

Comparison of Intelligent Fuzzy Controller and Fuzzy Rule Suram Algorithms in the Drying Process

Both the Intelligent Fuzzy Controller and Suram Fuzzy Rule are using a look-up table such as defuzzification analysis, which is based on weather variables, namely ambient temperature and ambient humidity for a drying process. State variable membership functions are expressed in terms of error values and error changes with typical triangular maps and trapezoidal maps. An Intelligent fuzzy controller is a hybrid controller which consists of an optimal fuzzy controller, fuzzy controller, and adaptive fuzzy controller. Membership function design is used to build the algorithm process. The algorithm process was developed based on the input-output knowledge pair for the drying process. The membership function must be stable and flexible with respect to weather and performance derived from the acquisition process, time constants, and system delays. The developed control system involves temperature control in different zones and ambient humidity. The system model was developed using a system identification scheme based on online input/output data and knowledge gathered through extensive testing. The knowledge base of fuzzy tuners is derived from drying schedules for certain wood specimens. The intelligent fuzzy control algorithm is used for scheduling the controller on various drying schedules. The results show that the proposed approach to overall control has great potential for performance improvement when applied to other industrial kilns. An intelligent fuzzy controller is also implemented, and its performance is compared with conventional controllers, it is more smooth, robust and controllable. On the other hand, the Suram fuzzy rule is an algorithm developed to control a drying system using diesel as an energy source by modifying the value of the fuzzy membership function [0.5,1]; and has been developed taking into account the wind speed in the drying process. The comparison results show that the Fuzzy Rule Suram is more efficient than the Intelligent Fuzzy Controller in terms of the use of electrical energy, by maximizing the use of solar energy

Fuzzy Guidance, Navigation and Control of a Spacecraft Simulator

To further facilitate the development of the guidance, navigation, and control systems of the future extra-planetary vehicles, there is a need for a simplified, easy-to-repair test bed that is dynamically similar to the full scale spacecraft. To achieve such a platform, a 3:1 thrust-to-weight ratio modular simulator was designed. The simulator is constructed from high strength-low density composite materials coupled with hobby grade electronic motors and a custom flexible landing gear system to increase stability and reduce capsizing while landing.For attitude control, a nonlinear Fuzzy Logic style control system was developed and analyzed against more traditional PID style control schemes used in the past generations. This new style of controller offers increased performance in attitude control. After a comprehensive and complete simulation analysis, the fuzzy logic controller was implemented using the open source computer BeagleBone Black. Feedback was deliver by the use of an inertial measurement unit In addition to the development of a fuzzy logic attitude control system, work began on the development of a full guidance, navigation, and control (GNC) system. The GNC system that was developed was a trajectory controller in the form of a fuzzy logic cascade control law. The simplified control law was developed to mimic the control systems used in commercial aircraft autopilots, in which the trajectory is assumed to be 2D, where the spacecraft simulator remains pointing in the direction of its destination point. The controller was developed to accept different styles of trajectory and the entire system is modular in nature.From the simulation analysis of the closed-loop system, system level design specification were determined for the flight hardware. Ultimately, after programming the controller and integrating the electronics, it was determined the total time-delay of the system exceeded the design specification. Because of the hardware limitations, the attitude controller was, at best, n neutrally stable. Future work is proposed to integrate a real time microcontroller to account for the limitations of the BeagleBone and programming language chosen

ARTMAP-FTR: A Neural Network For Fusion Target Recognition, With Application To Sonar Classification

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657

ARTMAP-FTR: A Neural Network for Object Recognition Through Sonar on a Mobile Robot

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657

Robust H∞ fuzzy output-feedback control with multiple probabilistic delays and multiple missing measurements

Copyright [2010] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, the robust H∞-control problem is investigated for a class of uncertain discrete-time fuzzy systems with both multiple probabilistic delays and multiple missing measurements. A sequence of random variables, all of which are mutually independent but obey the Bernoulli distribution, is introduced to account for the probabilistic communication delays. The measurement-missing phenomenon occurs in a random way. The missing probability for each sensor satisfies a certain probabilistic distribution in the interval. Here, the attention is focused on the analysis and design of H∞ fuzzy output-feedback controllers such that the closed-loop Takagi-Sugeno (T-S) fuzzy-control system is exponentially stable in the mean square. The disturbance-rejection attenuation is constrained to a given level by means of the H∞-performance index. Intensive analysis is carried out to obtain sufficient conditions for the existence of admissible output feedback controllers, which ensures the exponential stability as well as the prescribed H∞ performance. The cone-complementarity-linearization procedure is employed to cast the controller-design problem into a sequential minimization one that is solved by the semi-definite program method. Simulation results are utilized to demonstrate the effectiveness of the proposed design technique in this paper.This work was supported in part by the Engineering and Physical Sciences Research Council, U.K., under Grant GR/S27658/01, in part by the Royal Society, U.K., in part by the National Natural Science Foundation of China under Grant 60825303, in part by the National 973 Project of China under Grant 2009CB320600, in part by the Heilongjiang Outstanding Youth Science Fund of China under Grant JC200809, in part by the Youth Science Fund of Heilongjiang Province of China under Grant QC2009C63, and in part by the Alexander von Humboldt Foundation of Germany

ART Neural Networks: Distributed Coding and ARTMAP Applications

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Dissipative Analysis and Synthesis of Control for TS Fuzzy Markovian Jump Neutral Systems

This paper is focused on stochastic stability and strictly dissipative control design for a class of Takagi-Sugeno (TS) fuzzy neutral time delayed control systems with Markovian jumps. The main aim of this paper is to design a strictly dissipative controller such that the closed-loop TS fuzzy control system is stochastically stable, and also the disturbance rejection attenuation is obtained to a given level by means of the H∞ performance index. Intensive analysis is carried out to obtain sufficient conditions for the existence of desired dissipative controller which ensures both the stochastic stability and the strictly dissipative performance. The main advantage of the proposed technique is that it is possible to obtain the dissipative controller with less control effort and also, as special cases, robust H∞ control with the prescribed H∞ performance under given constraints and passivity control can be obtained for the considered systems. Also, the existence condition of the fuzzy dissipative controller can be obtained in terms of linear matrix inequalities. Finally, a practical example based on truck-trailer model is provided to demonstrate the effectiveness and feasibility of the proposed design technique