Adaptive Neural Constraint Output Control for a Class of Quantized Input Switched Nonlinear System

IEEE Access

Chong Lin¹  Zhiliang Liu¹  Bing Chen¹  Yun Shang² 

[1] Institute of Complexity Science, Qingdao University, Qingdao, China;School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China;

关键词: Adaptive neural control;    asymmetric constraint;    backstepping;    quantized control;    switched systems;   

DOI  :  10.1109/ACCESS.2019.2937822

来源: DOAJ

【 摘 要 】

In this paper, an adaptive neural control issue is addressed for a class of switched unknown strict-feedback nonlinear system under constraint output, in which the input signal is quantized. The control goal is to design a quantized controller to ensure that the system's output signal follows a given reference signal, meanwhile, the system output signal meets the asymmetric constraint requirement. To this end, the radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear functions. Adaptive backstepping technique and barrier Lyapunov function method are utilized to design the tracking controller and analyze the closed-loop stability. The proposed control strategy is shown to deal with the presented problem well. Finally, two simulation examples are presented to illustrate the efficacy of the design scheme.

【 授权许可】

Unknown