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

• URL: http://arxiv.org/abs/2510.09042v1
• Date: Fri, 10 Oct 2025 06:23:36 GMT
• Title: MAKO: Meta-Adaptive Koopman Operators for Learning-based Model Predictive Control of Parametrically Uncertain Nonlinear Systems
• Authors: Minghao Han, Kiwan Wong, Adrian Wing-Keung Law, Xunyuan Yin,
• Abstract summary: An adaptive deep meta-learning-based modeling approach, called Meta Adaptive Koopman Operator (MAKO), is proposed.<n>Our proposed approach demonstrates superior performance in both modeling accuracy and control efficacy as compared to competitive baselines.
• Score: 5.760286686122843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A robust and adaptive MPC formulation for Gaussian process models [2.6661512675766037]
  We present a robust and adaptive model predictive control (MPC) framework for uncertain nonlinear systems.<n>We derive robust predictions for GP models using metrics, which are incorporated in the MPC formulation.<n>We provide a numerical example of a planar quadrotor subject to difficult-to-model ground effects.
  arXiv  Detail & Related papers  (2025-07-02T19:27:14Z)
• An Iterative Bayesian Approach for System Identification based on Linear Gaussian Models [70.75865595489691]
  We tackle the problem of system identification, where we select inputs, observe the corresponding outputs from the true system, and optimize the parameters of our model to best fit the data.<n>Our approach only requires input-output data from the system and first-order information of the model with respect to the parameters.
  arXiv  Detail & Related papers  (2025-01-28T01:57:51Z)
• Fuzzy Model Identification and Self Learning with Smooth Compositions [1.9573380763700716]
  This paper develops a smooth model identification and self-learning strategy for dynamic systems.<n>We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface.
  arXiv  Detail & Related papers  (2024-12-31T20:19:02Z)
• MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models [6.031205224945912]
  A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a deep encoder network learns the nonlinearity from data. This transforms the nonlinear system into a linear system in a latent space, enabling the application of model predictive control (MPC) to determine effective control actions.
  arXiv  Detail & Related papers  (2024-04-18T11:29:43Z)
• Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control [16.703859991393568]
  This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty.
  arXiv  Detail & Related papers  (2024-04-15T07:30:26Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study [56.283944756315066]
  We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.
  arXiv  Detail & Related papers  (2024-01-09T11:54:54Z)
• End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control [45.84205238554709]
  We present a method for reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We show that the end-to-end trained models outperform those trained using system identification in (e)NMPC.
  arXiv  Detail & Related papers  (2023-08-03T10:21:53Z)
• Learning Stable Koopman Embeddings [9.239657838690228]
  We present a new data-driven method for learning stable models of nonlinear systems. We prove that every discrete-time nonlinear contracting model can be learnt in our framework.
  arXiv  Detail & Related papers  (2021-10-13T05:44:13Z)
• COMBO: Conservative Offline Model-Based Policy Optimization [120.55713363569845]
  Uncertainty estimation with complex models, such as deep neural networks, can be difficult and unreliable. We develop a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-actions. We find that COMBO consistently performs as well or better as compared to prior offline model-free and model-based methods.
  arXiv  Detail & Related papers  (2021-02-16T18:50:32Z)
• Control as Hybrid Inference [62.997667081978825]
  We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference. We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
  arXiv  Detail & Related papers  (2020-07-11T19:44:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.