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Enhancing data-driven control for sampled-data systems with polytopic disturbances
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DOI：10.1016/j.automatica.2026.112874.png)
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Abstract
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This paper advances data-driven control techniques for linear sampled-data systems subject to process disturbances in the data. It introduces a matrix-polytopic data-based system representation, modeling the disturbance as a convex polytopic constraint, which offers increased consistency with the noise data compared to existing methods in the case of pointwise-in-time ∞ -norm bounded noise. Furthermore, the paper proposes a combination-type extended looped-functional (cELF) approach, enabling more flexible stability conditions that do not necessitate positive definiteness and continuity at sampling points. By integrating cELF with the matrix-polytopic data-based representation, the paper derives a fresh data-based stability criterion expressed as linear matrix inequalities (LMIs), enhancing the effectiveness of data-driven control design. Numerical examples demonstrate that the proposed model- and data-based conditions allow for larger maximum sampling intervals (MSIs) than existing results, ensuring stability under designed controllers.
Keywords:
data-driven control
sampled-data systems
polytopic disturbances
linear matrix inequalities
stability analysis
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
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