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Koopman-based feedback design with stability guarantees

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arxiv 2312.01441 v2 pith:QAEZZFJZ submitted 2023-12-03 eess.SY cs.SYmath.OC

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keywords designcontrollerfeedbackstabilitysystemsattractiondataguarantees
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We present a method to design a state-feedback controller ensuring exponential stability for nonlinear systems using only measurement data. Our approach relies on Koopman-operator theory and uses robust control to explicitly account for approximation errors due to finitely many data samples. To simplify practical usage across various applications, we provide a tutorial-style exposition of the feedback design and its stability guarantees for single-input systems. Moreover, we extend this controller design to multi-input systems and more flexible nonlinear state-feedback controllers using gain-scheduling techniques to increase the guaranteed region of attraction. As the proposed controller design is framed as a semidefinite program, it allows for an efficient solution. Further, we enhance the geometry of the region of attraction through a heuristic algorithm that establishes a connection between the employed Koopman lifting and the dynamics of the system. Finally, we validate the proposed feedback design procedure by means of numerical examples.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Data-driven Koopman Operator-based Prediction and Control Using Model Averaging

    math.OC 2024-12 conditional novelty 5.0 of 10

    Koopman Model Averaging blends an ensemble of linear Koopman embeddings with data-based Bayesian weights and produces a single weighted linear model for prediction and control.

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