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

2025-03-25

AI Summary

To address the challenge of jointly ensuring safety and optimizing performance when tuning control barrier function (CBF) parameters in safety-critical control systems, this paper proposes an adaptive parameter-tuning framework integrating CBFs with safety-aware Bayesian optimization (SBO). Our method introduces a novel safety-acquisition function based on the barrier interior-point method, establishes the first taxonomy for CBF parameters, and provides unified theoretical guarantees for both safety satisfaction and optimality. The framework is model-agnostic and supports flexible customization of objectives and constraints. Evaluated on benchmark tasks—including swing-up control of an inverted pendulum and high-fidelity adaptive cruise control—the approach achieves 100% safety-constraint satisfaction while significantly improving closed-loop performance. Results demonstrate its rigorous safety assurance, real-time feasibility, and superior optimization efficiency.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Optimizing CBF parameters for safety-critical control performance
• Ensuring feasibility and safety in Bayesian optimization adaptation
• Model-agnostic framework for flexible safety control design

Innovation

• Combines CBF with Bayesian optimization
• Uses barrier-based interior method
• Works in model-agnostic environments