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.

中文翻译：

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贝叶斯优化的安全高效的无模型自适应控制

当精确的系统模型或控制器的合适参数可用时，自适应控制方法可产生高性能的控制器。现有的用于自适应控制的数据驱动方法大多使用有关动态不确定性或干扰的附加信息来增强基于标准模型的方法。在这项工作中，我们提出了一种纯粹的数据驱动，无模型的自适应控制方法。仅基于系统数据调整底层控制器引起了对底层算法安全性和计算性能的关注。因此，我们的方法基于GoOSE（一种用于安全且高效采样的贝叶斯优化算法）。我们在GoOSE中引入了几种计算和算法修改，使其可以在旋转运动系统上实际使用。我们用数值方法证明了几种类型的干扰，我们的方法具有高效的样本效率，在安全性方面优于受限的贝叶斯优化，并能通过网格评估实现性能优化。我们进一步证明了在旋转运动系统上实验提出的自适应控制方法。