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. Note to Practitioners—This work is motivated by developing a comprehensive control and optimization framework for high-precision motion systems. Precision motion is an integral application of advanced mechatronics and a cornerstone technology for high-value industrial processes such as semiconductor manufacturing. 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. Our method includes a variable to incorporate contextual information, which we name the task parameter. Using this variable, users can input external changes, such as changing step sizes for a motion system, or changing weight on top of the motion system. We provide two parallel implementation variants of our framework to make it suitable for run-time operation under context changes and applicable for continuous operation in industrial systems. We demonstrate the optimization framework on simulated examples and experiment on an industrial motion system to showcase its applicability in practice.