Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles

Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles

Zhang, Q. (TU Delft Transport Engineering and Logistics; Sun Yat-sen University)
Pan, W. (TU Delft Robot Dynamics)
Reppa, V. (TU Delft Transport Engineering and Logistics)

2021

This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines a conventional control method with reinforcement learning to enhance control accuracy and intelligence. In the proposed control design, a nominal system is considered for the design of a baseline tracking controller using a conventional control approach. The nominal system also defines the desired behaviour of uncertain autonomous surface vehicles in an obstacle-free environment. Thanks to reinforcement learning, the overall tracking controller is capable of compensating for model uncertainties and achieving collision avoidance at the same time in environments with obstacles. In comparison to 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 using an example of autonomous surface vehicles.

Analytical models
Autonomous surface vehicles
Collision avoidance
collision avoidance
control architecture.
reinforcement learning
Reinforcement learning
Stability analysis
Tracking
Trajectory
Uncertainty

http://resolver.tudelft.nl/uuid:baa90103-b858-4782-852a-3bdf24a09fa2

https://doi.org/10.1109/TITS.2021.3086033

2021-12-15

1524-9050

IEEE Transactions on Intelligent Transportation Systems, 23 (7), 8770-8781

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-care Otherwise 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.

Institutional Repository

journal article

© 2021 Q. Zhang, W. Pan, V. Reppa