Safe and Efficient Model-free Adaptive Control via Bayesian Optimization

Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrization 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. However, tuning low-level controllers based solely on system data raises concerns about 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 the underlying algorithm 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