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

• URL: http://arxiv.org/abs/2110.07479v1
• Date: Thu, 14 Oct 2021 15:51:03 GMT
• Title: VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
• Authors: Wenjie Xu, Colin N Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty
• Abstract summary: We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. We propose a violation-aware BO algorithm (VABO) that optimize closed-loop performance while simultaneously learning constraint-feasible solutions. We demonstrate the effectiveness of our proposed VABO method for energy minimization of industrial vapor compression systems.
• Score: 2.205124036454768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Unlike classical constrained BO methods which allow an 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 VABO method for energy minimization of industrial vapor compression systems.

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