Data-driven model-free sliding mode learning control for a class of discrete-time nonlinear systems

被引：5
|
作者：
Cao, Lei [1 ]
Gao, Shouli [1 ]
Zhao, Dongya [1 ]
机构：
[1] China Univ Petr, Coll New Energy, Changjiangxi Rd 66, Qingdao 266580, Shandong, Peoples R China
来源：
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL | 2020年 / 42卷 / 13期
基金：
中国国家自然科学基金;
关键词：
Data-driven; model-free; sliding mode learning control; discrete-time nonlinear systems;
D O I：
10.1177/0142331220921022
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a data-driven model-free sliding mode learning control (MFSMLC) for a class of discrete-time nonlinear systems. In this scheme, the control design does not depend on the mathematical model of the controlled system. The nonlinear system can be transformed into a dynamic linear data system by a novel dynamic linearization method. A recursive learning control algorithm is designed for the nonlinear system that can drive the sliding variable reach and remain on the sliding surface only by using output and input data. Moreover, the chattering is reduced because there is no non-smooth term in MFSMLC. After the strict stability analysis, the effectiveness of MFSMLC is validated by MATLAB simulations.
引用
收藏
页码：2533 / 2547
页数：15
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