Adaptive backstepping sliding mode control for nonlinear systems with neural networks

Authors:
Hongmei Zhang;Guoshan Zhang
Affiliations:
School of Electrical Engineering & Automation, Tianjin University, Tianjin, China and Shenyang Institute of Aeronautical Engineering, Shenyang, China;School of Electrical Engineering & Automation, Tianjin University, Tianjin, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

The backstepping control is investigated for a class of unknown nonlinear systems in parametric-pure-feedback form. Neural networks(NNs) are applied to approximate the unknown dynamics. The adaptive laws of the weights of NN and the ideal sliding mode are derived in the sense of Lyapunov function, so the stability can be guaranteed. The proposed control not only relaxes the assumptions of nonlinear systems, but also holds the robustness. Moreover, the tracking error can converge to zero asymptotically. Simulations illustrate the effectiveness of the proposed approach.