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.
翻译:当有精确系统模型或适当的控制器匹配时,适应性控制方法产生高性能控制器。现有的适应性控制数据驱动方法主要增强标准的模型方法,并提供关于动态或扰动的不确定性的额外信息。在这项工作中,我们提出了一个纯粹以数据驱动的、无模型控制的适应性控制方法。仅以系统数据为基础的低级控制器引起了对基本算法安全和计算性能的关切。因此,我们的方法以GOOSE为基础,这是安全和抽样高效的巴耶西亚优化的算法。我们在GOOSE中引入了若干计算和算法修改方法,使其能够在旋转运动系统上实际使用。我们从数字上表明,我们的方法在安全方面是有效的样本,优于受限制的巴伊斯优化,并实现了由电网评价计算的性能选择。我们进一步展示了在旋转运动系统上拟议的适应性控制方法。