Title: DSC-backstepping based robust adaptive NN control for strict-feedback nonlinear systems via small gain theorem

Authors: T.S. Li, B.G. Hong, G.Y. Shi

Addresses: School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China; Navigation College, Dalian Maritime University, Dalian, China. ' Navigation College, Dalian Maritime University, Dalian, China. ' Navigation College, Dalian Maritime University, Dalian, China

Abstract: The adaptive tracking control problem is discussed for a class of strict-feedback uncertain systems. RBF Neural Networks are used to approximate the uncertainties. A unified and systematic procedure is developed to derive a robust adaptive tracking controller with the fusion of dynamic surface control technique and small gain approach. The proposed algorithm can avoid both problems of |explosion of complexity| and |curse of dimension| synchronously, thus is convenient to implement in applications. The stability of the closed-loop system is proved. Finally, simulation results via two application examples validate the effectiveness and performance of the proposed scheme.

Keywords: uncertain systems; neural networks; dynamic surface control; small gain theorem; robust control; adaptive control; tracking control; simulation.

DOI: 10.1504/IJSCC.2008.019587

International Journal of Systems, Control and Communications, 2008 Vol.1 No.1, pp.124 - 145

Published online: 17 Jul 2008 *

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