mlsanityData to 7 Oct 2026

MAKO: Meta-Adaptive Koopman Operators for Learning-based Model Predictive Control of Parametrically Uncertain Nonlinear Systems

arXiv 2510.09042 ↗First posted 10 Oct 2025eess.SY cs.LG cs.SYcited by 1

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Abstract (arXiv)

In this work, we propose a meta-learning-based Koopman modeling and predictive control approach for nonlinear systems with parametric uncertainties. An adaptive deep meta-learning-based modeling approach, called Meta Adaptive Koopman Operator (MAKO), is proposed. Without knowledge of the parametric uncertainty, the proposed MAKO approach can learn a meta-model from a multi-modal dataset and efficiently adapt to new systems with previously unseen parameter settings by using online data. Based on the learned meta Koopman model, a predictive control scheme is developed, and the stability of the closed-loop system is ensured even in the presence of previously unseen parameter settings. Through extensive simulations, our proposed approach demonstrates superior performance in both modeling accuracy and control efficacy as compared to competitive baselines.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 42 references that are in the map. First posted on arXiv 10 Oct 2025.

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20 of 42 references.

References · 20

  1. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  2. 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 270
  3. 2016Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive controlcited by 134
  4. 2017Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systemscited by 125
  5. 2020Forecasting Sequential Data using Consistent Koopman Autoencoderscited by 114
  6. 2018Deep Dynamical Modeling and Control of Unsteady Fluid Flowscited by 67
  7. 2016Generalizing Koopman Theory to allow for inputs and controlcited by 49
  8. 2020Deep Learning of Koopman Representation for Controlcited by 25
  9. 2022Deep Koopman Operator with Control for Nonlinear Systemscited by 25
  10. 2024Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQRcited by 14
  11. 2022Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approachcited by 10
  12. 2022Koopman Form of Nonlinear Systems with Inputscited by 10
8 more
  1. 2022Deep Koopman Learning of Nonlinear Time-Varying Systemscited by 10
  2. 2021Robust Tube-based Model Predictive Control with Koopman Operators--Extended Versioncited by 9
  3. 2022Koopman-Based Neural Lyapunov Functions for General Attractorscited by 7
  4. 2023Convergence of Gradient-based MAML in LQRcited by 5
  5. 2013On Periodic Reference Tracking Using Batch-Mode Reinforcement Learning with Application to Gene Regulatory Network Controlcited by 3
  6. 2019Adaptive Model Predictive Control for A Class of Constrained Linear Systems with Parametric Uncertaintiescited by 3
  7. 2024Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processescited by 2
  8. 2019Self-Triggered Adaptive Model Predictive Control of Constrained Nonlinear Systems: A Min-Max Approachcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Multi-Task Learning of Conditional Mean Operators: applications to dynamical systems and uncertainty quantificationcited by 0