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Data-driven stabilization for linear sampled-data systems with unknown parameters: A pure data analytics perspective
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DOI：10.1016/j.neucom.2025.129798.png)
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Abstract
En 中文
This paper investigates the sampled-data stabilization problem for a class of linear systems with unknown parameters, incorporating a data analytics perspective. Traditional sampled-data controller design methods are rendered ineffective due to the lack of knowledge about the system parameters. To address this challenge, a data-driven approach is proposed for designing a sampled-data stabilization controller using an offlinecollected data set, eliminating the need for prior knowledge of the plant parameters. Specifically, a novel sampled-data control strategy with a matrix exponential gain is introduced to stabilize the system. The controller gain is designed based on data insights derived from offline information, without requiring access to the system matrices. Leveraging data-driven techniques and Lyapunov stability theory, sufficient conditions are established to guarantee the exponential stability of the system. The proposed sampled-data controller with matrix exponential gain significantly extends the maximum allowable sampling intervals compared to conventional methods. To optimize performance, a convex optimization approach is utilized to maximize the bounds of these sampling intervals. The efficacy of the proposed approach is validated through a practical example involving an operational amplifier circuit, demonstrating the power of integrating data analytics into control design for unknown-parameter systems.
Keywords:
Data-driven control
Unknown linear systems
Sampled-data control
Matrix exponential gain
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6.5
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2.5W
Citations:
6.5W
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