# Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization

arXiv: https://arxiv.org/abs/2503.19349 · HTML: https://mlsanity.com/paper/2503.19349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 26 of 26)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [A Lyapunov-based Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/1805.07708/) (2018)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator](https://mlsanity.com/paper/1801.05039/) (2018)
- [Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances](https://mlsanity.com/paper/1709.07523/) (2017)
- [GPflow: A Gaussian process library using TensorFlow](https://mlsanity.com/paper/1610.08733/) (2016)
- [Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics](https://mlsanity.com/paper/1602.04450/) (2016)
- [Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions](https://mlsanity.com/paper/2010.16001/) (2020)
- [Control Barrier Functions for Systems with High Relative Degree](https://mlsanity.com/paper/1903.04706/) (2019)
- [Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations](https://mlsanity.com/paper/2111.09971/) (2021)
- [Robot Learning with Crash Constraints](https://mlsanity.com/paper/2010.08669/) (2020)
- [Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization](https://mlsanity.com/paper/2010.15211/) (2020)
- [Funneled Bayesian Optimization for Design, Tuning and Control of Autonomous Systems](https://mlsanity.com/paper/1610.00366/) (2016)
- [Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments](https://mlsanity.com/paper/2201.01347/) (2022)
- [Prediction-Correction Interior-Point Method for Time-Varying Convex Optimization](https://mlsanity.com/paper/1608.07544/) (2016)
- [Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping](https://mlsanity.com/paper/1903.09816/) (2019)
- [Constrained Bayesian Optimization Under Partial Observations: Balanced Improvements and Provable Convergence](https://mlsanity.com/paper/2312.03212/) (2023)
- [Recursive Feasibility Guided Optimal Parameter Adaptation of Differential Convex Optimization Policies for Safety-Critical Systems](https://mlsanity.com/paper/2109.10949/) (2021)
- [Safety-Aware Preference-Based Learning for Safety-Critical Control](https://mlsanity.com/paper/2112.08516/) (2021)
- [Safe Bayesian Optimization using Interior-Point Methods -- Applied to Personalized Insulin Dose Guidance](https://mlsanity.com/paper/2205.02327/) (2022)
- [C-GLISp: Preference-Based Global Optimization under Unknown Constraints with Applications to Controller Calibration](https://mlsanity.com/paper/2106.05639/) (2021)
- [Trajectory Generation by Chance Constrained Nonlinear MPC with Probabilistic Prediction](https://mlsanity.com/paper/2006.07907/) (2020)
- [Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimation](https://mlsanity.com/paper/2111.13849/) (2021)
- [Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization](https://mlsanity.com/paper/2203.15305/) (2022)

## Abstract (arXiv)

Safety is of paramount importance in control systems to avoid costly risks and catastrophic damages. The control barrier function (CBF) method, a promising solution for safety-critical control, poses a new challenge of enhancing control performance due to its direct modification of original control design and the introduction of uncalibrated parameters. In this work, we shed light on the crucial role of configurable parameters in the CBF method for performance enhancement with a systematical categorization. Based on that, we propose a novel framework combining the CBF method with Bayesian optimization (BO) to optimize the safe control performance. Considering feasibility/safety-critical constraints, we develop a safe version of BO using the barrier-based interior method to efficiently search for promising feasible configurable parameters. Furthermore, we provide theoretical criteria of our framework regarding safety and optimality. An essential advantage of our framework lies in that it can work in model-agnostic environments, leaving sufficient flexibility in designing objective and constraint functions. Finally, simulation experiments on swing-up control and high-fidelity adaptive cruise control are conducted to demonstrate the effectiveness of our framework.