# Preference-based MPC calibration

arXiv: https://arxiv.org/abs/2003.11294 · HTML: https://mlsanity.com/paper/2003.11294/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [GLISp-r: A preference-based optimization algorithm with convergence guarantees](https://mlsanity.com/paper/2202.01125/) (2022)
- [Learning Critical Scenarios in Feedback Control Systems for Automated Driving](https://mlsanity.com/paper/2209.12586/) (2022)
- [Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Styles](https://mlsanity.com/paper/2503.15407/) (2025)
- [Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models](https://mlsanity.com/paper/2603.24138/) (2026)
- [Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents](https://mlsanity.com/paper/2507.13491/) (2025)

## Abstract (arXiv)

Automating the calibration of the parameters of a control policy by means of global optimization requires quantifying a closed-loop performance function. As this can be impractical in many situations, in this paper we suggest a semi-automated calibration approach that requires instead a human calibrator to express a preference on whether a certain control policy is "better" than another one, therefore eliminating the need of an explicit performance index. In particular, we focus our attention on semi-automated calibration of Model Predictive Controllers (MPCs), for which we attempt computing the set of best calibration parameters by employing the recently-developed active preference-based optimization algorithm GLISp. Based on the preferences expressed by the human operator, GLISp learns a surrogate of the underlying closed-loop performance index that the calibrator (unconsciously) uses and proposes, iteratively, a new set of calibration parameters to him or her for testing and for comparison against previous experimental results. The resulting semi-automated calibration procedure is tested on two case studies, showing the capabilities of the approach in achieving near-optimal performance within a limited number of experiments.