Journal
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
Volume -, Issue -, Pages 9782-9788Publisher
IEEE
DOI: 10.1109/ICRA48506.2021.9561349
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Funding
- Swiss Innovation Agency [46716]
- Swiss National Science Foundation under NCCR Automation
- ETH Max Planck Center for Learning Systems
- European Unions [815943]
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This paper proposes a purely data-driven, model-free approach for adaptive control, which builds on the GoOSE algorithm and demonstrates practical use 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.
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