VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints

Understand

We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics.

• Bayesian optimization (BO) has been demonstrated effective for improving closed-loop performance by automatically tuning controller gains or reference setpoints in a model-free manner.
• However, BO methods have rarely been tested on dynamical systems with unmodeled constraints.
• In this paper, we propose a violation-aware BO algorithm (VABO) that optimizes closed-loop performance while simultaneously learning constraint-feasible solutions.