Open-closed-loop iterative learning control for a class of nonlinear systems with random data dropouts

International Conference on Computer Information and Automation Engineering

计算机科学;运输工程

Cheng, X.Y.^1 ; Wang, H.B.^1 ; Jia, Y.L.^1 ; Dong, Y.H.^1

Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao

066004, China^1

关键词: Bernoulli random variables;    Convergence criterion;    Iterative learning control;    Iterative learning Control (ILC);    Measurement and control;    Open loop control;    Remote controllers;    Rigorous analysis;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/359/1/012009/pdf
DOI  :  10.1088/1757-899X/359/1/012009

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In this paper, an open-closed-loop iterative learning control (ILC) algorithm is constructed for a class of nonlinear systems subjecting to random data dropouts. The ILC algorithm is implemented by a networked control system (NCS), where only the off-line data is transmitted by network while the real-time data is delivered in the point-to-point way. Thus, there are two controllers rather than one in the control system, which makes better use of the saved and current information and thereby improves the performance achieved by open-loop control alone. During the transfer of off-line data between the nonlinear plant and the remote controller data dropout occurs randomly and the data dropout rate is modeled as a binary Bernoulli random variable. Both measurement and control data dropouts are taken into consideration simultaneously. The convergence criterion is derived based on rigorous analysis. Finally, the simulation results verify the effectiveness of the proposed method.