Robust adaptive neural tracking control for a class of nonlinear systems with unmodeled dynamics using disturbance observer

NEUROCOMPUTING

卷:292

Article

Wang, Xinjun¹  Yin, Xinghui¹  Shen, Fei¹ 

[1] Hohai Univ, Coll Comp & Informat, Nanjing 211100, Jiangsu, Peoples R China

关键词: Adaptive backstepping control;    Unmodeled dynamics;    Dead zone;    Disturbance observer;    Radial basis function neural networks(RBFNNs);   

DOI  :  10.1016/j.neucom.2018.02.082

来源: Elsevier

【 摘 要 】

This paper is concerned with an adaptive neural tracking control for a class of strict-feedback nonlinear systems subject to unmodeled dynamics, system uncertainties, completely unknown external disturbance and input dead zone. An adaptive neural control method combined with backstepping technique and the radial basis function neural networks (RBFNNs) is proposed for the systems under consideration. In recursive backstepping designs, a dynamic signal is introduced to cope with the unmodeled dynamics, a disturbance observer is employed to approximate the unknown disturbance and the dead zone equalled to the sum of the simple linear system and the partial bounded disturbance. It is shown that by using Lyapunov methods, the developed control scheme can ensure semi-globally uniformly ultimately bounded (SGUUB) of all signals within the closed-loop systems. Simulation results are presented to illustrate the validity of the approach. (c) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

Free