Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems

被引:88
|
作者
Lin, Chih-Min [1 ]
Chen, Te-Yu [1 ]
机构
[1] Yuan Ze Univ, Dept Elect Engn, Tao Yuan 320, Taiwan
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2009年 / 20卷 / 09期
关键词
Cerebellar model articulation controller (CMAC); gradient-descent method; Lyapunov stability theorem; self-organizing; uncertain nonlinear systems; NEURAL-NETWORK CONTROL;
D O I
10.1109/TNN.2009.2013852
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a self-organizing control system based on cerebellar model articulation controller (CMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding-mode control (SMC), so the input space dimension of CMAC can be simplified. The structure of CMAC will be self-organized; that is, the layers of CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The control system consists of a self-organizing CMAC (SOCM) and a robust controller. SOCM containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient-descent method is used to online tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. A simulation study of inverted double pendulums system and an experimental result of linear ultrasonic motor motion control show that favorable tracking performance can be achieved by using the proposed control system.
引用
收藏
页码:1377 / 1384
页数:8
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