Indirect self-tuning control using multiple models for non-affine nonlinear systems

被引:24
作者
Fu, Yue [1 ]
Chai, Tianyou [1 ]
机构
[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China
关键词
indirect self-tuning control; non-affine system; multiple models; stability; ADAPTIVE-CONTROL; GLOBAL CONVERGENCE;
D O I
10.1080/00207179.2011.588960
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this article, for a class of discrete time non-affine nonlinear systems, a multiple-model-based indirect self-tuning control method is developed. The indirect self-tuning control method is composed of a linear robust indirect self-tuning controller, a nonlinear neural network indirect self-tuning controller and a switching mechanism. By introducing a modified Clarke index, the indirect self-tuning control method can tolerate properties of non-minimum phase and open-loop instability of the controlled system and attenuate the disturbance caused by the higher order nonlinear term. By resorting to the time-varying operation, it is proved without the assumption on the boundedness of the nonlinear term or its differential term that the proposed multiple-model-based nonlinear indirect self-tuning control method can guarantee the bounded-input-bounded-output stability of the closed-loop system and improve the system performance simultaneously. To illustrate the effectiveness of the proposed method, simulations for a synthetic nonlinear system and a realistic nonlinear system are conducted.
引用
收藏
页码:1031 / 1040
页数:10
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