TR2022-064
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
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- , "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints", American Control Conference (ACC), June 2022, pp. 5288-5293.BibTeX TR2022-064 PDF
- @inproceedings{Xu2022jun,
- author = {Xu, Wenjie and Jones, Colin and Svetozarevic, Bratislav and Laughman, Christopher R. and Chakrabarty, Ankush},
- title = {VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- pages = {5288--5293},
- month = jun,
- isbn = {978-1-6654-5197-0},
- url = {https://www.merl.com/publications/TR2022-064}
- }
- , "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints", American Control Conference (ACC), June 2022, pp. 5288-5293.
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MERL Contacts:
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Research Areas:
Abstract:
We study the problem of performance optimiza- tion 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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Related Publication
BibTeX arXiv
- @article{Xu2021oct,
- author = {Xu, Wenjie and Jones, Colin and Svetozarevic, Bratislav and Laughman, Christopher R. and Chakrabarty, Ankush},
- title = {VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints},
- journal = {arXiv},
- year = 2021,
- month = oct,
- url = {https://arxiv.org/abs/2110.07479}
- }