A new data-driven sliding mode learning control for discrete-time MIMO linear systems

被引：0
|
作者：
Cao L. [1 ]
Gao S. [1 ]
Zhao D. [1 ]
机构：
[1] College of New Energy, China University of Petroleum, Qingdao
来源：
International Journal of Industrial and Systems Engineering | 2022年 / 42卷 / 02期
基金：
中国国家自然科学基金;
关键词：
data-driven; discrete-time MIMO linear systems; parameter estimation algorithm; sliding mode learning control;
D O I：
10.1504/ijise.2022.126074
中图分类号：
学科分类号：
摘要：
A new data-driven sliding mode learning control (DDSMLC) is designed for a class of discrete-time MIMO linear systems in the presence of uncertainties. In this scheme, a new control is designed to enforce the states to reach and remain on the sliding surface. In addition, a recursive algorithm using system measured data is adopted to estimate the unknown system parameters, so a complete data-driven sliding mode control is designed, which does not need to know any parameters in the system. Moreover, the chattering is reduced because there is no non-smooth control used in DDSMLC. After the strict stability analysis, the effectiveness of DDSMLC is validated by MATLAB simulations. Copyright © 2022 Inderscience Enterprises Ltd.
引用
收藏
页码：211 / 229
页数：18
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