# Adaptive Bayesian Optimization for High-Precision Motion Systems

arXiv: https://arxiv.org/abs/2404.14602 · HTML: https://mlsanity.com/paper/2404.14602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 38 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics](https://mlsanity.com/paper/1602.04450/) (2016)
- [Safe Controller Optimization for Quadrotors with Gaussian Processes](https://mlsanity.com/paper/1509.01066/) (2015)
- [Information Directed Sampling and Bandits with Heteroscedastic Noise](https://mlsanity.com/paper/1801.09667/) (2018)
- [Safe Exploration for Interactive Machine Learning](https://mlsanity.com/paper/1910.13726/) (2019)
- [Time-Varying Gaussian Process Bandit Optimization](https://mlsanity.com/paper/1601.06650/) (2016)
- [Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning](https://mlsanity.com/paper/1906.12086/) (2019)
- [Bayesian Learning-Based Adaptive Control for Safety Critical Systems](https://mlsanity.com/paper/1910.02325/) (2019)
- [Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization](https://mlsanity.com/paper/2010.15211/) (2020)
- [On Controller Tuning with Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2207.11120/) (2022)
- [Safe and Efficient Model-free Adaptive Control via Bayesian Optimization](https://mlsanity.com/paper/2101.07825/) (2021)
- [Performance-Driven Cascade Controller Tuning with Bayesian Optimization](https://mlsanity.com/paper/2007.12536/) (2020)
- [Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization](https://mlsanity.com/paper/2103.15416/) (2021)
- [Cascade Control: Data-Driven Tuning Approach Based on Bayesian Optimization](https://mlsanity.com/paper/2005.03970/) (2020)
- [Learning-Based Repetitive Precision Motion Control with Mismatch Compensation](https://mlsanity.com/paper/2111.10246/) (2021)
- [Benchmark of Bayesian Optimization and Metaheuristics for Control Engineering Tuning Problems with Crash Constraints](https://mlsanity.com/paper/2211.02571/) (2022)
- [Safe Risk-averse Bayesian Optimization for Controller Tuning](https://mlsanity.com/paper/2306.13479/) (2023)
- [Efficient safe learning for controller tuning with experimental validation](https://mlsanity.com/paper/2310.17431/) (2023)

## Abstract (arXiv)

Controller tuning and parameter optimization are crucial in system design to improve closed-loop system performance. Bayesian optimization has been established as an efficient model-free controller tuning and adaptation method. However, Bayesian optimization methods are computationally expensive and therefore difficult to use in real-time critical scenarios. In this work, we propose a real-time purely data-driven, model-free approach for adaptive control, by online tuning low-level controller parameters. We base our algorithm on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization, for handling performance and stability criteria. We introduce multiple computational and algorithmic modifications for computational efficiency and parallelization of optimization steps. We further evaluate the algorithm's performance on a real precision-motion system utilized in semiconductor industry applications by modifying the payload and reference stepsize and comparing it to an interpolated constrained optimization-based baseline approach.