Decentralized adaptive fuzzy neural iterative learning control for nonaffine nonlinear interconnected systems

✍ Scribed by Ying-Chung Wang; Chiang-Ju Chien

• Publisher: John Wiley and Sons
• Year: 2010
• Tongue: English
• Weight: 260 KB
• Volume: 13
• Category: Article
• ISSN: 1561-8625
• DOI: 10.1002/asjc.299

✦ Synopsis

Abstract

In this paper, we study the design of iterative learning controllers for nonaffine nonlinear interconnected systems with repeatable control tasks. The interaction between each subsystem can be a general type of unknown nonlinear function if a bounding condition is satisfied. An error model is derived such that only local subsystem information is required for the controller design. An adaptive iterative learning controller for each subsystem is constructed based on a fuzzy neural learning component and a robust learning component. The fuzzy neural learning component designed by an output recurrent fuzzy neural network is utilized as an approximator to approximate the system nonaffine nonlinearities and interconnections. The approximation error due to the fuzzy neural learning component will be then compensated by a robust learning component. Stable adaptive laws are derived to update the control parameters in order to guarantee the stability and convergence. We show that the internal signals are bounded during the learning process and the state tracking errors of each subsystem converge asymptotically along the iteration axis to a tunable residual set.

Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society