安全なリスク回避ベイズ最適化によるコントローラチューニング
Safe Risk-averse Bayesian Optimization for Controller Tuning
異分散ノイズ下で安全制約を満たしながらコントローラをチューニングするデータ駆動手法RaGoOSEを提案し、合成ベンチマークと半導体産業の精密モーションシステムで有効性を示した。
著者: Christopher Koenig, Miks Ozols, Anastasia Makarova, Efe C. Balta, Andreas Krause, Alisa Rupenyan
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
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.
