# A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization

arXiv: https://arxiv.org/abs/2011.11841 · HTML: https://mlsanity.com/paper/2011.11841/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 16 of 16)

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- [Performance-driven Constrained Optimal Auto-Tuner for MPC](https://mlsanity.com/paper/2503.07127/) (2025)
- [Online Control-Informed Learning](https://mlsanity.com/paper/2410.03924/) (2024)
- [An Automatic Tuning MPC with Application to Ecological Cruise Control](https://mlsanity.com/paper/2309.09358/) (2023)
- [Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging](https://mlsanity.com/paper/2404.06125/) (2024)
- [Adaptive Model Predictive Control by Learning Classifiers](https://mlsanity.com/paper/2203.06783/) (2022)
- [Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain](https://mlsanity.com/paper/2408.09253/) (2024)
- [Nonmyopic Global Optimisation via Approximate Dynamic Programming](https://mlsanity.com/paper/2412.04882/) (2024)
- [Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents](https://mlsanity.com/paper/2507.13491/) (2025)
- [Direct transfer of optimized controllers to similar systems using dimensionless MPC](https://mlsanity.com/paper/2512.08667/) (2025)
- [ASIA: an Autonomous System Identification Agent](https://mlsanity.com/paper/2605.10480/) (2026)
- [Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC](https://mlsanity.com/paper/2609.01061/) (2026)
- [Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering](https://mlsanity.com/paper/2610.02014/) (2026)

## Abstract (arXiv)

The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach for automating the tuning of MPC under uncertainty. In particular, we formulate the automated tuning problem as a constrained black-box optimization problem that can be tackled with derivative-free optimization. We rely on a constrained variant of Bayesian optimization (BO) to solve the MPC tuning problem that can directly handle noisy and expensive-to-evaluate functions. The benefits of the proposed automated tuning approach are demonstrated on a benchmark continuously stirred tank reactor example.