# Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study

arXiv: https://arxiv.org/abs/1812.06325 · HTML: https://mlsanity.com/paper/1812.06325/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2018.

## Built on by (most cited first, 15 of 15)

- [Meta-Learning Priors for Safe Bayesian Optimization](https://mlsanity.com/paper/2210.00762/) (2022)
- [On Controller Tuning with Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2207.11120/) (2022)
- [Explainable Bayesian Optimization](https://mlsanity.com/paper/2401.13334/) (2024)
- [Event-Triggered Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2208.10790/) (2022)
- [A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems](https://mlsanity.com/paper/2109.10898/) (2021)
- [Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach](https://mlsanity.com/paper/2211.00077/) (2022)
- [BayeSQP: Bayesian Optimization through Sequential Quadratic Programming](https://mlsanity.com/paper/2602.03232/) (2026)
- [Automated Controller Calibration by Kalman Filtering](https://mlsanity.com/paper/2111.10832/) (2021)
- [Combining Large Language Models and Gradient-Free Optimization for Automatic Control Policy Synthesis](https://mlsanity.com/paper/2510.00373/) (2025)
- [Sonic: A Sampling-based Online Controller for Streaming Applications](https://mlsanity.com/paper/2108.10701/) (2021)
- [Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning](https://mlsanity.com/paper/2209.04854/) (2022)
- [Parallel Bayesian Optimization Using Satisficing Thompson Sampling for Time-Sensitive Black-Box Optimization](https://mlsanity.com/paper/2310.12526/) (2023)
- [Automatic Parameter Tuning of Self-Driving Vehicles](https://mlsanity.com/paper/2406.17757/) (2024)
- [Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems](https://mlsanity.com/paper/2408.05992/) (2024)
- [Local Preferential Bayesian Optimization](https://mlsanity.com/paper/2606.02351/) (2026)

## Abstract (arXiv)

Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model the unknown function from controller parameters to a user-defined cost. The probabilistic model is updated with data, which is obtained by testing a set of parameters on the physical system and evaluating the cost. In order to learn fast, the Bayesian optimization algorithm selects the next parameters to evaluate in a systematic way, for example, by maximizing information gain about the optimum. The algorithm thus iteratively finds the globally optimal parameters with only few experiments. Taking throttle valve control as a representative industrial control example, the proposed auto-tuning method is shown to outperform manual calibration: it consistently achieves better performance with a low number of experiments. The proposed auto-tuning framework is flexible and can handle different control structures and objectives.