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

arXiv: https://arxiv.org/abs/2301.12099 · HTML: https://mlsanity.com/paper/2301.12099/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 28 Jan 2023.

## Builds on (most cited first, 20 of 20)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1403.5607/) (2014)
- [Stagewise Safe Bayesian Optimization with Gaussian Processes](https://mlsanity.com/paper/1806.07555/) (2018)
- [Safe Controller Optimization for Quadrotors with Gaussian Processes](https://mlsanity.com/paper/1509.01066/) (2015)
- [Safe Exploration for Interactive Machine Learning](https://mlsanity.com/paper/1910.13726/) (2019)
- [Constrained Efficient Global Optimization of Expensive Black-box Functions](https://mlsanity.com/paper/2211.00162/) (2022)
- [Cost-aware Bayesian Optimization](https://mlsanity.com/paper/2003.10870/) (2020)
- [Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning](https://mlsanity.com/paper/1906.12086/) (2019)
- [Performance-oriented model learning for data-driven MPC design](https://mlsanity.com/paper/1904.10839/) (2019)
- [Goal-Driven Dynamics Learning via Bayesian Optimization](https://mlsanity.com/paper/1703.09260/) (2017)
- [Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization](https://mlsanity.com/paper/2010.15211/) (2020)
- [VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints](https://mlsanity.com/paper/2110.07479/) (2021)
- [Lower Bounds on the Worst-Case Complexity of Efficient Global Optimization](https://mlsanity.com/paper/2209.09655/) (2022)
- [Waypoint Optimization Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systems](https://mlsanity.com/paper/1910.12901/) (2019)
- [Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures](https://mlsanity.com/paper/2005.07443/) (2020)
- [A data-driven robust optimization approach to scenario-based stochastic model predictive control](https://mlsanity.com/paper/1807.05146/) (2018)
- [Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization](https://mlsanity.com/paper/2005.05888/) (2020)
- [CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints](https://mlsanity.com/paper/2211.11822/) (2022)
- [Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization](https://mlsanity.com/paper/1909.05699/) (2019)

## Built on by (most cited first, 6 of 6)

- [Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints](https://mlsanity.com/paper/2304.06104/) (2023)
- [Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach](https://mlsanity.com/paper/2310.00758/) (2023)
- [Bayesian Optimization of Expensive Nested Grey-Box Functions](https://mlsanity.com/paper/2306.05150/) (2023)
- [What price to pay? Auto-tuning a building MPC controller for optimal economic cost](https://mlsanity.com/paper/2501.10859/) (2025)
- [Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints](https://mlsanity.com/paper/2310.00962/) (2023)
- [Exploiting Hierarchical Controller Structure in Contextual Parameter Learning for Humanoid Loco-Manipulation](https://mlsanity.com/paper/2610.04609/) (2026)

## Abstract (arXiv)

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.