Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

Pramana

Yu-Zhang Zhao¹  Xing-Yuan Wang²  Abdurahman Kadir1 2² 

[1] School of Computer Science and Engineering, Xinjiang University of Finance and Economics, Urumchi 830012, China$$;Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China$$

关键词: Chaos control;    adaptive control;    adaptive identifier;    fuzzy neural network;    backpropagation algorithm.;   

DOI  :  

学科分类：物理（综合）

来源: Indian Academy of Sciences

【 摘 要 】

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.

【 授权许可】

Unknown