Optimal and stable fuzzy controllers for nonlinear systems based on an improved genetic algorithm
ABSTRACT This paper addresses the optimization and stabilization problems of nonlinear systems subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and an improved genetic algorithm (GA). The TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller is then obtained to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the improved GA. In order to obtain the optimal fuzzy controller, the membership functions are further tuned by minimizing a defined fitness function using the improved GA. An application example on stabilizing a two-link robot arm will be given.
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ABSTRACT: This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It is also shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.IEEE Transactions on Neural Networks 02/2003; · 2.95 Impact Factor
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ABSTRACT: The stability analysis and the design technique of fuzzy control systems using fuzzy block diagrams are discussed. First, we show the concept of fuzzy blocks and consider the connection problems of fuzzy blocks diagrams. We derive some theorems and corollaries with respect to two basic types of connections of fuzzy blocks. In order to preserve some properties in a connection of fuzzy blocks, continuous piecewise-polynomial membership functions are defined. Secondly, a sufficient condition which guarantees the stability of fuzzy systems is obtained in terms of Lyapunov's direct method. We give an important fact based on this condition. Thirdly, we propose a new design technique of a fuzzy controller. The fuzzy block diagrams and the stability analysis are applied to the design problems of a model-based fuzzy controller.Fuzzy Sets and Systems. 01/1992;
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ABSTRACT: Presents the stability and robustness analysis for multivariable fuzzy control systems subject to parameter uncertainties based on a single-grid-point (SGP) approach. To perform the analysis, we represent a multivariable nonlinear system using a TS-fuzzy plant model. Three design approaches of fuzzy controllers are introduced to close the feedback loop. By estimating the matrix measures of the system parameters and parameter uncertainties, stability and robustness conditions for different cases are derived. Application examples are given to show the design procedures and the merits of the proposed fuzzy controllerIEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans 12/2000; · 2.18 Impact Factor