mlsanityData to 14 Oct 2026

Stability-informed Bayesian Optimization for MPC Cost Function Learning

arXiv 2404.12187 ↗First posted 18 Apr 2024eess.SY cs.LG cs.SYcited by 8

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Abstract (arXiv)

Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predictive control parameters under imperfect information while considering closed-loop stability. We employ constrained Bayesian optimization to learn a model predictive controller's (MPC) cost function parametrized as a feedforward neural network, optimizing closed-loop behavior as well as minimizing model-plant mismatch. Doing so offers a high degree of freedom and, thus, the opportunity for efficient and global optimization towards the desired and optimal closed-loop behavior. We extend this framework by stability constraints on the learned controller parameters, exploiting the optimal value function of the underlying MPC as a Lyapunov candidate. The effectiveness of the proposed approach is underlined in simulations, highlighting its performance and safety capabilities.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 5 of its 18 references that are in the map. First posted on arXiv 18 Apr 2024.

Builds on

5 of 18 references.

References · 5

  1. 2020Neural Lyapunov Controlcited by 142
  2. 2017Cautious Model Predictive Control using Gaussian Process Regressioncited by 91
  3. 2019Safe Reinforcement Learning Using Robust MPCcited by 85
  4. 2016Safe Learning of Regions of Attraction for Uncertain, Nonlinear Systems with Gaussian Processescited by 39
  5. 2019Performance-oriented model learning for data-driven MPC designcited by 22

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 2.

8 papers.

  1. 2024Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Controlcited by 7
  2. 2025A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Controlcited by 2
  3. 2025Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Stylescited by 1
  4. 2026Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Modelscited by 1
  5. 2024Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Chargingcited by 0
  6. 2025Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agentscited by 0
  7. 2025High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Controlcited by 0
  8. 2026Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimizationcited by 0