Disturbance observer based adaptive neural prescribed performance control for a class of uncertain nonlinear systems with unknown backlash-like hysteresis

NEUROCOMPUTING

卷:299

Article

Wang, Xinjun¹  Yin, Xinghui¹  Shen, Fei¹ 

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

关键词: Prescribed performance;    Adaptive backstepping control;    Disturbance observer;    Backlash-like hysteresis;    Radial basis function neural networks(RBFNNs);   

DOI  :  10.1016/j.neucom.2018.02.088

来源: Elsevier

【 摘 要 】

In this paper, an adaptive neural tracking control is studied for a class of strict-feedback nonlinear systems with guaranteed predefined performance subject to unknown backlash-like hysteresis input, uncertain parameters and external unknown disturbance. 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, the tracking control performance can be guaranteed by exploiting a new performance function. A disturbance observer is employed to approximate the unknown disturbance. It is shown that by using Lyapunov methods, the designed controller can guarantee the prespecified transient and 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.

【 授权许可】

Free