Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints

TL;DR

This paper introduces VACBO, a violation-aware Bayesian optimization method that efficiently optimizes control system performance under unmodeled, time-varying constraints by allowing controlled violations to enhance learning.

Contribution

The paper proposes a novel violation-aware contextual Bayesian optimization algorithm that balances constraint violations and performance to improve optimization in dynamic environments.

Findings

VACBO effectively minimizes energy in vapor compression systems.

It adapts to changing ambient temperature and humidity.

It accelerates constraint learning compared to traditional methods.

Abstract

We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be 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 and time-varying ambient conditions. In this paper, we propose a violation-aware contextual BO algorithm (VACBO) that optimizes closed-loop performance while simultaneously learning constraint-feasible solutions under time-varying ambient conditions. Unlike classical constrained BO methods which allow unlimited constraint violations, or 'safe' BO algorithms that are conservative and try to operate with near-zero violations, we allow budgeted constraint violations to improve constraint learning and accelerate optimization. We demonstrate the effectiveness of our proposed VACBO method for energy minimization of industrial vapor compression systems under time-varying ambient temperature and humidity.