# Performance-driven Constrained Optimal Auto-Tuner for MPC

arXiv: https://arxiv.org/abs/2503.07127 · HTML: https://mlsanity.com/paper/2503.07127/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 21 of 21)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [On Information Gain and Regret Bounds in Gaussian Process Bandits](https://mlsanity.com/paper/2009.06966/) (2020)
- [Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics](https://mlsanity.com/paper/1602.04450/) (2016)
- [Maximizing acquisition functions for Bayesian optimization](https://mlsanity.com/paper/1805.10196/) (2018)
- [Safe Controller Optimization for Quadrotors with Gaussian Processes](https://mlsanity.com/paper/1509.01066/) (2015)
- [Safe Exploration for Interactive Machine Learning](https://mlsanity.com/paper/1910.13726/) (2019)
- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [A survey on policy search algorithms for learning robot controllers in a handful of trials](https://mlsanity.com/paper/1807.02303/) (2018)
- [AMZ Driverless: The Full Autonomous Racing System](https://mlsanity.com/paper/1905.05150/) (2019)
- [Submodular Reinforcement Learning](https://mlsanity.com/paper/2307.13372/) (2023)
- [A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization](https://mlsanity.com/paper/2011.11841/) (2020)
- [Cautious Bayesian Optimization for Efficient and Scalable Policy Search](https://mlsanity.com/paper/2011.09445/) (2020)
- [Near-Optimal Multi-Agent Learning for Safe Coverage Control](https://mlsanity.com/paper/2210.06380/) (2022)
- [Safe Guaranteed Exploration for Non-linear Systems](https://mlsanity.com/paper/2402.06562/) (2024)
- [Contextual Tuning of Model Predictive Control for Autonomous Racing](https://mlsanity.com/paper/2110.02710/) (2021)
- [AutoTune: Controller Tuning for High-Speed Flight](https://mlsanity.com/paper/2103.10698/) (2021)
- [Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control](https://mlsanity.com/paper/2209.12048/) (2022)
- [Weighted Maximum Likelihood for Controller Tuning](https://mlsanity.com/paper/2210.11087/) (2022)
- [Lipschitz Safe Bayesian Optimization for Automotive Control](https://mlsanity.com/paper/2501.12969/) (2025)

## Built on by (most cited first, 6 of 6)

- [Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics](https://mlsanity.com/paper/2505.07594/) (2025)
- [Safe and Near-Optimal Control with Online Dynamics Learning](https://mlsanity.com/paper/2509.16650/) (2025)
- [Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control](https://mlsanity.com/paper/2512.03772/) (2025)
- [SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks](https://mlsanity.com/paper/2602.16187/) (2026)
- [Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation](https://mlsanity.com/paper/2606.14763/) (2026)
- [Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond](https://mlsanity.com/paper/2609.12660/) (2026)

## Abstract (arXiv)

A key challenge in tuning Model Predictive Control (MPC) cost function parameters is to ensure that the system performance stays consistently above a certain threshold. To address this challenge, we propose a novel method, COAT-MPC, Constrained Optimal Auto-Tuner for MPC. With every tuning iteration, COAT-MPC gathers performance data and learns by updating its posterior belief. It explores the tuning parameters' domain towards optimistic parameters in a goal-directed fashion, which is key to its sample efficiency. We theoretically analyze COAT-MPC, showing that it satisfies performance constraints with arbitrarily high probability at all times and provably converges to the optimum performance within finite time. Through comprehensive simulations and comparative analyses with a hardware platform, we demonstrate the effectiveness of COAT-MPC in comparison to classical Bayesian Optimization (BO) and other state-of-the-art methods. When applied to autonomous racing, our approach outperforms baselines in terms of constraint violations and cumulative regret over time.