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.

翻译：控制器调参及参数优化对于提升闭环系统性能至关重要。贝叶斯优化已被证实为一种高效的无模型控制器调参及自适应方法。然而，贝叶斯优化方法计算代价高昂，难以应用于实时性要求高的场景。本研究提出一种基于纯数据驱动的实时无模型自适应控制方法，通过在线调节底层控制器参数实现性能优化。该算法以GoOSE（一种兼顾安全性与样本效率的贝叶斯优化算法）为基础，可处理性能与稳定性约束条件。我们通过引入多项计算与算法改进，实现了优化步骤的高效计算与并行化处理。最后，在半导体工业实际使用的高精度运动系统上，通过改变负载与参考步长，将所提算法与基于插值约束优化的基线方法进行对比，评估了该算法的性能表现。