A new discrete-time robust adaptive time-delay control for a class of uncertain nonlinear strict-feedback systems using sliding mode

Mohammadpour, Omid; Tavasoli, Ali

doi:10.1016/j.isatra.2019.03.005

Cited by 6 publications

(1 citation statement)

References 23 publications

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“…In addition, almost all the control algorithms need to be implemented by computers, so it is more meaningful to study the control of discrete-time nonlinear systems. There are many control methods for discrete-time nonlinear systems, such as adaptive control (Mohammadpour and Tavasoli, 2019), predictive control (Hu and Ding, 2019), and sliding mode control (Ghabi and Dhouibi, 2018; Raja et al, 2018; Tsai et al, 2018; Xiong et al, 2019), to name just a few. Among them, discrete-time sliding mode control (DSMC) has been widely applied and studied owing to its strong robustness, quick response and simple design.…”

Section: Introductionmentioning

confidence: 99%

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

Cao

Gao

Zhao

2020

Transactions of the Institute of Measurement and Control

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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.

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