Affiliation of Author(s):自动化学院
Journal:MATHEMATICAL PROBLEMS IN ENGINEERING
Key Words:ADAPTIVE NEURAL-CONTROL TRACKING CONTROL CONTROL SCHEME IDENTIFICATION NETWORKS OBSERVER
Abstract:This paper presented a new data-driven robust control scheme for unknown nonlinear systems in the presence of input saturation and external disturbances. According to the input and output data of the nonlinear system, a recurrent neural network (RNN) data-driven model is established to reconstruct the dynamics of the nonlinear system. An adaptive output-feedback controller is developed to approximate the unknown disturbances and a novel input saturation compensation method is used to attenuate the effect of the input saturation. Under the proposed adaptive control scheme, the uniformly ultimately bounded convergence of all the signals of the closed-loop nonlinear system is guaranteed via Lyapunov analysis. The simulation results are given to show the effectiveness of the proposed data-driven robust controller.
ISSN No.:1024-123X
Translation or Not:no
Date of Publication:2017-01-01
Co-author:王丽,Gong Huajun,lcs
Correspondence Author:王莉,Wang Li