Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks.
A direct adaptive state-feedback controller is proposed for highly nonlinear systems. We consider uncertain or ill-defined nonaffine nonlinear systems and employ a neural network (NN) with flexible structure, i.e., an online variation of the number of neurons. The NN approximates and adaptively cancels an unknown plant nonlinearity. A control law and adaptive laws for the weights in the hidden layer and output layer of the NN are established so that the whole closed-loop system is stable in the sense of Lyapunov. Moreover, the tracking error is guaranteed to be uniformly asymptotically stable (UAS) rather than uniformly ultimately bounded (UUB) with the aid of an additional robustifying control term. The proposed control algorithm is relatively simple and requires no restrictive conditions on the design constants for the stability. The efficiency of the proposed scheme is shown through the simulation of a simple nonaffine nonlinear system.
Park
Jang-Hyun
JH
Department of Control System Engineering, Mokpo National University, Chonnam 534-729, Korea. jhpark72@mokpo.ac.kr
Huh
Sung-Hoe
SH
Kim
Seong-Hwan
SH
Seo
Sam-Jun
SJ
Park
Gwi-Tae
GT
D000222:Adaptation, Physiological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics