ベイズ最適化による安全で効率的なモデルフリー適応制御
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
安全でサンプル効率の高いベイズ最適化アルゴリズムGoOSEを改良し、システムデータのみから低レベル制御器をチューニングするモデルフリー適応制御手法を提案、回転運動系で数値的・実験的に有効性を示した。
著者: Christopher König, Matteo Turchetta, John Lygeros, Alisa Rupenyan, Andreas Krause
分類: eess.SY, cs.AI, cs.LG, cs.RO, cs.SY
原文アブストラクト
Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrizations of the controller are available. Existing data-driven approaches for adaptive control mostly augment standard model-based methods with additional information about uncertainties in the dynamics or about disturbances. In this work, we propose a purely data-driven, model-free approach for adaptive control. Tuning low-level controllers based solely on system data raises concerns on the underlying algorithm safety and computational performance. Thus, our approach builds on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization. We introduce several computational and algorithmic modifications in GoOSE that enable its practical use on a rotational motion system. We numerically demonstrate for several types of disturbances that our approach is sample efficient, outperforms constrained Bayesian optimization in terms of safety, and achieves the performance optima computed by grid evaluation. We further demonstrate the proposed adaptive control approach experimentally on a rotational motion system.
