Control of a class of nonlinear systems based on self-organizing fuzzy neural

作者

Li, An-Ping (1, 2); Liu, Guo-Rong (1, 3);

期刊:Dianji yu Kongzhi Xuebao/Electric Machines and Control ISSN:1007449X , 年:2016 . 卷:20 . 期:12 页码:82-91

语种: Chinese

原文链接:http://doi.org/10.15938/j.emc.2016.12.011

摘要

This paper discusses the problem of tracking control a class of uncertain MIMO nonlinear system with disturbances and unknown control gain sign. An improved algorithm for online self-organizing fuzzy neural network is proposed to overcome the difficulty of choosing parameters and based on it, a robust and adaptive controller is proposed. Firstly, the MIMO system was decompounded into several SISO systems, and then the improved self-organizing adaptive fuzzy neural network was utilized to approximate the unknown function with the structure and parameters being tuned online. Then a Nussbaum function was adopted to overcome the difficulty of the unknown control gain sign, and a robust control term and an error estimation term were utilized to compensate for the errors. The closed-loop control system was proved to be semi-globally, uniformly, and ultimately bounded by Lyapunov stability theorem. Theoretical analysis and numerical simulations show the effectiveness of the developed approach. © 2016, Harbin University of Science and Technology Publication. All right reserved.

附加关键词:
Closed loop control systems - Fuzzy inference - Fuzzy logic - Fuzzy neural networks - MIMO systems - Nonlinear systems - Online systems - Robust control

作者信息

通讯作者:
     Li, An-Ping
作者机构:
     [1] College of Electrical and Information Engineering, Hunan University, Changsha; 410082, China
     [2] College of Science, Hunan Institute of Engineering, Xiangtan; 411104, China
     [3] College of Electrical and Information, Hunan Institute of Engineering, Xiangtan; 411104, China