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Guaranteed Cost Intermittent Control for Discrete-Time System: A Data-Driven Method
Yi Zou
Engang Tian*
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Submitted: 10 Sept 2023 | Accepted: 27 Nov 2023 | Published: 24 Sept 2024

Abstract

This paper explores a data-driven method to investigate the stabilization of intermittent controlled discrete-time systems (ICDTSs) with unknown parameter matrices. First, the pre-collected inputstate data is used to supersede the accurate prior system model. Then, in order to obtain the data-dependent stabilization conditions of ICDTSs, a novel relationship is designed among the control width, rest width, and convergence rate. Unlike existing studies on the stabilization of ICDTSs, this paper only needs the collected input-state data. Thus, the time-consuming process of model identification is avoided. In addition, to ensure an acceptable performance level, the data-based guaranteed cost control is also considered, and a new cost function for ICDTSs is correspondingly built. Finally, two simulations are presented to demonstrate the effectiveness of the theoretical analysis.

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Zou, Y., & Tian, E. (2024). Guaranteed Cost Intermittent Control for Discrete-Time System: A Data-Driven Method. International Journal of Network Dynamics and Intelligence, 3(3), 100015. https://doi.org/10.53941/ijndi.2024.100015
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Copyright (c) 2024 by the authors.

This work is licensed under a This work is licensed under a Creative Commons Attribution 4.0 International License.

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