mlsanityData to 7 Oct 2026

Performance-oriented model learning for data-driven MPC design

arXiv 1904.10839 ↗First posted 23 Apr 2019math.OCcited by 22

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Abstract (arXiv)

Model Predictive Control (MPC) is an enabling technology in applications requiring controlling physical processes in an optimized way under constraints on inputs and outputs. However, in MPC closed-loop performance is pushed to the limits only if the plant under control is accurately modeled; otherwise, robust architectures need to be employed, at the price of reduced performance due to worst-case conservative assumptions. In this paper, instead of adapting the controller to handle uncertainty, we adapt the learning procedure so that the prediction model is selected to provide the best closed-loop performance. More specifically, we apply for the first time the above "identification for control" rationale to hierarchical MPC using data-driven methods and Bayesian optimization.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2019.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 6; 2024: 6; 2025: 3; 2026: 1.

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  1. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  2. 2021On Recurrent Neural Networks for learning-based control: recent results and ideas for future developmentscited by 14
  3. 2023From system models to class models: An in-context learning paradigmcited by 13
  4. 2021VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraintscited by 10
  5. 2024Stability-informed Bayesian Optimization for MPC Cost Function Learningcited by 8
  6. 2021Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learningcited by 7
  7. 2023No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximationscited by 6
  8. 2024Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Controlcited by 6
  9. 2023Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraintscited by 5
  10. 2023On the adaptation of in-context learners for system identificationcited by 5
  11. 2022Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertaintycited by 3
  12. 2022CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraintscited by 3
10 more
  1. 2024A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Controlcited by 3
  2. 2023Reinforcement Learning with Model Predictive Control for Highway Ramp Meteringcited by 2
  3. 2024Time-Series-Informed Closed-loop Learning for Sequential Decision Making and Controlcited by 1
  4. 2022Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learningcited by 0
  5. 2024Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learningcited by 0
  6. 2024Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Chargingcited by 0
  7. 2025Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agentscited by 0
  8. 2025Direct transfer of optimized controllers to similar systems using dimensionless MPCcited by 0
  9. 2025High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Controlcited by 0
  10. 2026Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Modelscited by 0