Abstract
This paper presents a novel model-reference reinforcement learning control method for uncertain autonomous surface vehicles. The proposed control combines a conventional model-based control method with deep reinforcement learning. With the conventional model-based control, we can ensure the learning-based control law provides closed-loop stability for the trajectory tracking control of the overall system, and increase the sample efficiency of the deep reinforcement learning. With reinforcement learning, we can directly learn a control law to compensate for modeling uncertainties. In the proposed control, a nominal system is employed for the design of a baseline control law using a conventional control approach. The nominal system also defines the desired performance for uncertain autonomous vehicles to follow. In comparison with traditional deep reinforcement learning methods, our proposed learning-based control can provide stability guarantees and better sample efficiency. We demonstrate the performance of the new algorithm via extensive simulation results.
Original language | English |
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Title of host publication | Proceedings of the 59th IEEE Conference on Decision and Control, CDC 2020 |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 5291-5296 |
ISBN (Electronic) | 978-1-7281-7447-1 |
DOIs | |
Publication status | Published - 2020 |
Event | 59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of Duration: 14 Dec 2020 → 18 Dec 2020 |
Conference
Conference | 59th IEEE Conference on Decision and Control, CDC 2020 |
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Country/Territory | Korea, Republic of |
City | Virtual, Jeju Island |
Period | 14/12/20 → 18/12/20 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.