Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
This work addresses the challenge of safe and efficient adaptive control for systems where precise models are unavailable, offering a practical solution for motion control applications, though it is incremental as it builds on existing Bayesian optimization methods.
The authors tackled the problem of tuning low-level controllers without a system model by proposing a purely data-driven, model-free adaptive control approach based on Bayesian optimization, demonstrating numerically that it is sample efficient, outperforms constrained Bayesian optimization in safety, and achieves performance optima, with experimental validation on a rotational motion system.
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.