Direct adaptive control for a class of uncertain nonlinear systems using neural networks

Authors:
Tingliang Hu;Jihong Zhu;Chunhua Hu;Zengqi Sun
Affiliations:
State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China;State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China;State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China;State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

This paper presents a direct adaptive control scheme based on multi-layer neural networks for a class of single-input-single-output (SISO) uncertain nonlinear systems. The on-line updating rules of the neural networks parameters are obtained by Lyapunov stability theory. All signals in the closed-loop system are bounded and the output tracking error converges to a small neighborhood of zero. In this sense the stability of the closed-loop system is guaranteed. The effectiveness of the control scheme is verified by a simulation of inverted pendulum.