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Data-driven robust reference governor for constrained nonlinear systems
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DOI：10.1080/00207179.2025.2575289.png)
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Abstract
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We present a data-driven robust reference governor (DRRG) framework that handles output constraints in nonlinear systems with unknown dynamics and uncertainties. By incorporating the Koopman operator, the DRRG first maps nonlinear system dynamics into a higher-dimensional linear space, allowing for more effective data-driven prediction. Subsequently, we develop a data-driven maximal admissible set (MAS) and a min-max optimisation problem using the high-dimensional data-driven predictors to compute optimal reference signals that are resilient to all possible realizations of uncertainties. The proposed DRRG method provides a practical solution to improving robustness against uncertainties while retaining the existing control architectures. Numerical simulations are presented to demonstrate the effectiveness of the approach.
Keywords:
Reference governor
data-driven control
nonlinear system
maximal admissible set
min-max optimisation
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1.6
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92
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AUTOMATICAIF5.9
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