Data-Driven Robust Control of Unknown MIMO Nonlinear System Subject to Input Saturations and Disturbances

被引:1
|
作者
Wang, Li [1 ,2 ]
Gong, Huajun [1 ]
Liu, Chunsheng [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 210016, Jiangsu, Peoples R China
[2] Nanhang Jincheng Coll, Nanjing 211156, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
ADAPTIVE NEURAL-CONTROL; TRACKING CONTROL; CONTROL SCHEME; IDENTIFICATION; OBSERVER;
D O I
10.1155/2017/5186025
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页数:11
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