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Modeling Nonlinear Control Systems via Koopman Control Family: Universal Forms and Subspace Invariance Proximity

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arxiv 2307.15368 v4 pith:NDDLQPBV submitted 2023-07-28 math.OC cs.SYeess.SY

classification math.OCcs.SYeess.SY
keywords controlsystemsinvariancemodelingmodelsnonlinearsubspaceconcept
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This paper introduces the Koopman Control Family (KCF), a mathematical framework for modeling general (not necessarily control-affine) discrete-time nonlinear control systems with the aim of providing a solid theoretical foundation for the use of Koopman-based methods in systems with inputs. We demonstrate that the concept of KCF captures the behavior of nonlinear control systems on a (potentially infinite-dimensional) function space. By employing a generalized notion of subspace invariance under the KCF, we establish a universal form for finite-dimensional models, which encompasses the commonly used linear, bilinear, and linear switched models as specific instances. In cases where the subspace is not invariant under the KCF, we propose a method for approximating models in general form and characterize the model's accuracy using the concept of invariance proximity. We end by discussing how the proposed framework naturally lends itself to data-driven modeling of control systems.

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Cited by 3 Pith papers

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

  1. Controller Design for Bilinear Neural Feedback Loops

    eess.SY 2025-05 conditional novelty 6.0 of 10

    The paper gives LMI-based controller synthesis guaranteeing local exponential stability for bilinear systems with neural networks in the loop.

  2. Data-Enabled Predictive Control for Nonlinear Systems Based on a Koopman Bilinear Realization

    math.OC 2025-05 conditional novelty 5.0 of 10

    A nonlinear extension of Willems' Fundamental Lemma via Koopman bilinear realization enables direct data-driven predictive control without EDMD-based system identification.

  3. 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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