Neural networks robust adaptive control for a class of MIMO uncertain nonlinear systems

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:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 3
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Abstract

This paper presents a robust adaptive control scheme for a class of multi-input multi-output (MIMO) uncertain nonlinear systems. Multiple multi-layer neural networks are used to compensate for the model uncertainty. 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. The output tracking error converges to a small neighborhood of zero, while the stability of the closed-loop system is guaranteed. Finally the effectiveness of the control scheme is verified by a simulation of two-link manipulator.