Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance. Bayesian optimization has been established as an efficient model-free method for controller tuning and adaptation. Standard methods, however, are not enough for high-precision systems to be robust with respect to unknown input-dependent noise and stable under safety constraints. In this work, we present a novel data-driven approach, RaGoOSE, for safe controller tuning in the presence of heteroscedastic noise, combining safe learning with risk-averse Bayesian optimization. We demonstrate the method for synthetic benchmark and compare its performance to established BO-based tuning methods. We further evaluate RaGoOSE performance on a real precision-motion system utilized in semiconductor industry applications and compare it to the built-in auto-tuning routine.

翻译：控制器调参与参数优化是系统设计中提升控制器及底层系统性能的关键环节。贝叶斯优化已被证实是一种高效的控制器调参与自适应无模型方法。然而，标准方法难以确保高精度系统在未知输入相关噪声下的鲁棒性，并满足安全约束下的稳定性要求。本研究提出一种新型数据驱动方法RaGoOSE，用于存在异方差噪声场景下的安全控制器调参，该方法将安全学习与风险厌恶贝叶斯优化相结合。我们在合成基准测试中验证了该方法，并将其性能与基于贝叶斯优化的经典调参方法进行对比。进一步，我们针对半导体工业领域应用的精密运动系统评估了RaGoOSE的性能，并将其与内置自整定程序进行了比较。