mlsanityData to 7 Oct 2026

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

arXiv 2110.07479 ↗First posted 14 Oct 2021cs.LG cs.SY eess.SYcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

14 of 32 references.

References · 14

  1. 2018A Tutorial on Bayesian Optimizationcited by 734
  2. 2014Bayesian Optimization with Unknown Constraintscited by 109
  3. 2018Stagewise Safe Bayesian Optimization with Gaussian Processescited by 79
  4. 2015Safe Controller Optimization for Quadrotors with Gaussian Processescited by 77
  5. 2019Safe Exploration for Interactive Machine Learningcited by 51
  6. 2020Cost-aware Bayesian Optimizationcited by 41
  7. 2019Performance-oriented model learning for data-driven MPC designcited by 22
  8. 2017Goal-Driven Dynamics Learning via Bayesian Optimizationcited by 19
  9. 2020Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimizationcited by 15
  10. 2019Waypoint Optimization Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systemscited by 6
  11. 2020Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failurescited by 6
  12. 2020Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guaranteescited by 5
2 more
  1. 2020Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimizationcited by 3
  2. 2019Closed-loop Model Selection for Kernel-based Models using Bayesian Optimizationcited by 2

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Cited by year

Citing papers per year: 2022: 3; 2023: 6; 2024: 1.

10 papers.

  1. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  2. 2024Principled Preferential Bayesian Optimizationcited by 16
  3. 2022Lower Bounds on the Worst-Case Complexity of Efficient Global Optimizationcited by 7
  4. 2022Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approachcited by 5
  5. 2023Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraintscited by 5
  6. 2022CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraintscited by 3
  7. 2023Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraintscited by 3
  8. 2023Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approachcited by 3
  9. 2023Bayesian Optimization of Expensive Nested Grey-Box Functionscited by 1
  10. 2023Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraintscited by 0