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

Lei Cao, Shouli Gao and Dongya Zhao

International Journal of Industrial and Systems Engineering, 2022, vol. 42, issue 2, 211-229

Abstract: 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.

Keywords: data-driven; discrete-time MIMO linear systems; parameter estimation algorithm; sliding mode learning control. (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:42:y:2022:i:2:p:211-229

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