Neural network-based dynamic surface control of nonlinear systems with unknown virtual control coefficient

Authors:
Yingchun Wang;Guotao Hui;Zhiliang Wang;Huaguang Zhang
Affiliations:
Information Science and Engineering, Northeastern University, Shenyang, China;Information Science and Engineering, Northeastern University, Shenyang, China;Information Science and Engineering, Northeastern University, Shenyang, China;Information Science and Engineering, Northeastern University, Shenyang, China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Year:
2011

Citing 18
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Abstract

This paper is concerned with the adaptive control problem for a class of strict-feedback nonlinear systems, in which unknown virtual control gain function is the main feature. Based on the neural network approximate ability and backstepping control design technique, adaptive neural network based dynamic surface control technique is developed. The advantage is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time, by dynamic surface control scheme, the explosion of computation is circumvented. The control performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded and the control error converges to a small residual set around the origin.