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

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 a...

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Veröffentlicht in:International journal of control, automation, and systems Jg. 21; H. 7; S. 2293 - 2302
Hauptverfasser: Hou, Mingdong, Pan, Weigang, Han, Yaozhen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.07.2023
Springer Nature B.V
제어·로봇·시스템학회
Schlagworte:
Control
Control methods
Control systems design
Controllers
Convergence
Data models
Design parameters
Discrete time systems
Engineering
Iterative methods
Linearization
Mathematical analysis
Mechatronics
Nonlinear systems
Parameter estimation
Predictive control
Regular Papers
Robotics
Sliding mode control
Tracking errors
제어계측공학
integral sliding mode
Dynamic linearization
nonlinear system
iterative learning control
ISSN:1598-6446, 2005-4092
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Zusammenfassung: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.
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http://link.springer.com/article/10.1007/s12555-022-0229-y
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-022-0229-y