Reduced SA Fuzzy-neural Controller for Nonlinear Systems

• Main Authors: Jian-Hao Liao, 廖建豪
• Other Authors: Yih-Guang Leu
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/27418802411099300466

碩士 === 國立臺灣師範大學 === 工業教育學系 === 97 === In this thesis, a reduced simulated annealing algorithm used to tune the parameters of fuzzy neural networks is proposed for function approximation and adaptive control of nonlinear systems. For the design of adaptive controller, the reduced simulated annealing algorithm does not require the procedure of off-line learning and the complicated mathematical form. Compared with traditional adaptive controllers, computation loading can be effectively alleviated. In adaptive control procedure for nonlinear systems, the weights of the fuzzy neural controller are online adjusted by the reduced simulated annealing algorithm in order to generate the appropriate control input. For the purpose of on-line evaluating the stability of the closed-loop systems, an energy cost function derived from Lyapunov function is involved in the reduced simulated annealing algorithm. In addition, the system states may go into the unsafe region if the reduced simulated annealing algorithm can not instantaneously generate the appropriate weights. In order to guarantee the stability of the closed-loop nonlinear system, a supervisory controller is incorporated into the fuzzy neural controller. Finally, some computer simulation examples and a servo motor experiment are provided to demonstrate the feasibility and effectiveness of the proposed method.