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Online optimal control for dynamic positioning of vessels via time-based adaptive dynamic programming

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Abstract

In this paper, a discrete-time online optimal control via time-based adaptive dynamic programming is developed for dynamic positioning (DP) of vessels in the presence of unknown system dynamic, energy conservation and wastage decrease of equipment. Firstly, a model network is established by a neural network (NN) to identify the DP system. And then, a NN optimal control scheme is developed, in which current and recorded data of DP vessel are utilized to train the critic network and action network. The optimal cost and control law are updated once at the sampling instant. The designed DP optimal control policy can maintain the vessel at desired position and heading angle, and guarantee the uniform ultimate boundedness of all the signals in the closed-loop system simultaneously. Finally, simulation results involving a supply vessel demonstrate the effectiveness of the proposed DP optimal control law.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (under Grant Nos. 51939001, 61976033, 61751202, U1813203, 61803064), the Science and Technology Innovation Funds of Dalian (under Grant no. 2018J11CY022), the LiaoNing Revitalization Talents Program (under Grant no. XLYC1807046), Natural Foundation Guidance Plan Project of Liaoning (2019-ZD-0151) and the Fundamental Research Funds for the Central Universities (under Grant nos. 3132019345).

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Authors and Affiliations

  1. Navigation College, Dalian Maritime University, Dalian, China

    Xiaoyang Gao, Tieshan Li, Qihe Shan, Liang’en Yuan & Yifan Liu

  2. Department of Computer Science, The University of Alabama, Tuscaloosa, AL, 35487-0290, USA

    Yang Xiao

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  1. Xiaoyang Gao
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  2. Tieshan Li
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  3. Qihe Shan
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  4. Yang Xiao
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  5. Liang’en Yuan
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  6. Yifan Liu
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Correspondence to Tieshan Li.

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Gao, X., Li, T., Shan, Q. et al. Online optimal control for dynamic positioning of vessels via time-based adaptive dynamic programming. J Ambient Intell Human Comput 14, 15629–15641 (2023). https://doi.org/10.1007/s12652-019-01522-9

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  • DOI: https://doi.org/10.1007/s12652-019-01522-9

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