Koopman based data-driven predictive control

2021/02/09 by Ying Zhao Lian, Lian, Yingzhao, Renzi Wang +3 · 1 citation
Engineering · Physics and Astronomy · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #FOS: Mathematics #Model Reduction and Neural Networks #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2102.05122

openalex publication_date 2021/02/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Sparked by the Willems' fundamental lemma, a class of data-driven control methods has been developed for LTI systems. At the same time, the Koopman operator theory attempts to cast a nonlinear control problem into a standard linear one albeit infinite-dimensional. Motivated by these two ideas, a data-driven control scheme for nonlinear systems is proposed in this work. The proposed scheme is compatible with most differential regressors enabling offline learning. In particular, the model uncertainty is considered, enabling a novel data-driven simulation framework based on Wasserstein distance. Numerical experiments are performed with Bayesian neural networks to show the effectiveness of both the proposed control and simulation scheme.

Citations

• Data-driven simulation and control
• From model-based control to data-driven control: Survey, classification and perspective
• Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
• Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
• Data-driven stabilization of nonlinear polynomial systems with noisy data
• Nonlinear Data-Enabled Prediction and Control

Cited by

• A Time-Series Foundation Model by Universal Delay Embedding

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