Adaptive dynamic programming and deep reinforcement learning for the control of an unmanned surface vehicle: Experimental results
| Autor: | Leonardo Garrido, Alejandro Gonzalez-Garcia, Ivana Collado-Gonzalez, David Barragan-Alcantar |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Unmanned surface vehicle Artificial neural network Computer science Applied Mathematics 020208 electrical & electronic engineering Control (management) Control engineering 02 engineering and technology Computer Science Applications Dynamic programming 020901 industrial engineering & automation Control and Systems Engineering Control theory 0202 electrical engineering electronic engineering information engineering Backpropagation through time Reinforcement learning Electrical and Electronic Engineering Surge |
| Zdroj: | Control Engineering Practice. 111:104807 |
| ISSN: | 0967-0661 |
| DOI: | 10.1016/j.conengprac.2021.104807 |
| Popis: | This paper presents a low-level controller for an unmanned surface vehicle based on adaptive dynamic programming and deep reinforcement learning. This approach uses a single deep neural network capable of self-learning a policy, and driving the surge speed and yaw dynamics of a vessel. A simulation of the vehicle mathematical model was used to train the neural network with the model-based backpropagation through time algorithm, capable of dealing with continuous action-spaces. The path-following control scenario is additionally addressed by combining the proposed low-level controller and a line-of-sight based guidance law with time-varying look-ahead distance. Simulation and real-world experimental results are presented to validate the control capabilities of the proposed approach and contribute to the diversity of validated applications of adaptive dynamic programming based control strategies. Results show the controller is capable of self-learning the policy to drive the surge speed and yaw dynamics, and has an improved performance in comparison to a standard controller. |
| Databáze: | OpenAIRE |
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