Adaptive Bayesian Optimization for High-Precision Motion Systems

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 the semiconductor industry applications by modifying the payload and reference stepsize and comparing it to an interpolated constrained optimization-based baseline approach. The proposed method framework relies on data-driven optimization methods that can be designed by prescribing desired system performance. By using a method based on Bayesian Optimization and safe exploration, our method optimizes desired parameters based on the prescribed system performance. A key benefit is the incorporation of input and output constraints, which are satisfied throughout the optimization procedure. Therefore, the method is suitable for use in practical systems where safety or operational constraints are of concern.

Recommended citation: Christopher König, Raamadaas Krishnadas, Efe C. Balta, Alisa Rupenyan, IEEE Transactions on Automation Science and Engineering, Volume 22, Pages 15627–15637, 2025
Download Paper