Regular Papers

International Journal of Control, Automation and Systems 2023; 21(7): 2293-2302

Published online May 26, 2023

https://doi.org/10.1007/s12555-022-0229-y

© The International Journal of Control, Automation, and Systems

A Discrete-time Integral Sliding Mode Predictive Control Using Iterative Dynamic Linearization Data Model

Mingdong Hou, Weigang Pan*, and Yaozhen Han

Shandong Jiaotong University

Abstract

The current study presents a data-driven integral sliding mode predictive control method for a category of discrete-time repetitive nonlinear systems. At first, a compact form of iterative dynamic linearization (IDL) technology is utilized to establish an IDL data model. Then, considering the time and iterative domain simultaneously, an iterative integral sliding mode surface is constructed to establish an iterative integral sliding mode controller. The stability of the presented control strategy is then demonstrated through a precise mathematical analysis. Furthermore, to further reduce the control error, an iterative integral sliding mode predictive control strategy is established using the model predictive control. Since the proposed method is a data-driven control scheme, it only employs the online I/O data for parameter estimation and controller design. The effectiveness and monotonic convergence of the proposed schemes are evaluated through simulations. Comparative results with the data-driven optimal iterative learning controller (DDOILC) and the enhanced DDOILC indicate that the presented controller can provide a faster convergence and less tracking error.

Keywords Dynamic linearization, integral sliding mode, iterative learning control, nonlinear system.

Article

Regular Papers

International Journal of Control, Automation and Systems 2023; 21(7): 2293-2302

Published online July 1, 2023 https://doi.org/10.1007/s12555-022-0229-y

Copyright © The International Journal of Control, Automation, and Systems.

A Discrete-time Integral Sliding Mode Predictive Control Using Iterative Dynamic Linearization Data Model

Mingdong Hou, Weigang Pan*, and Yaozhen Han

Shandong Jiaotong University

Abstract

The current study presents a data-driven integral sliding mode predictive control method for a category of discrete-time repetitive nonlinear systems. At first, a compact form of iterative dynamic linearization (IDL) technology is utilized to establish an IDL data model. Then, considering the time and iterative domain simultaneously, an iterative integral sliding mode surface is constructed to establish an iterative integral sliding mode controller. The stability of the presented control strategy is then demonstrated through a precise mathematical analysis. Furthermore, to further reduce the control error, an iterative integral sliding mode predictive control strategy is established using the model predictive control. Since the proposed method is a data-driven control scheme, it only employs the online I/O data for parameter estimation and controller design. The effectiveness and monotonic convergence of the proposed schemes are evaluated through simulations. Comparative results with the data-driven optimal iterative learning controller (DDOILC) and the enhanced DDOILC indicate that the presented controller can provide a faster convergence and less tracking error.

Keywords: Dynamic linearization, integral sliding mode, iterative learning control, nonlinear system.

IJCAS
February 2024

Vol. 22, No. 2, pp. 347~729

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