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Article

Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking

by
Wei Li
1,
Jun Zhang
2,*,
Fang Wang
1,3 and
Hanyun Zhou
4
1
College of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China
2
School of Electrical Information Engineering, Jiangsu University, Zhenjiang 212013, China
3
Science and Technology on Underwater Vehicle Technology Laboratory, Harbin Engineering University, Harbin 150001, China
4
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2023, 11(12), 2283; https://doi.org/10.3390/jmse11122283
Submission received: 19 October 2023 / Revised: 21 November 2023 / Accepted: 28 November 2023 / Published: 30 November 2023
(This article belongs to the Section Ocean Engineering)
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Abstract

The underactuated unmanned surface vessel (USV) has been identified as a promising solution for future maritime transport. However, the challenges of precise trajectory tracking and obstacle avoidance remain unresolved for USVs. To this end, this paper models the problem of path tracking through the first-order Nomoto model in the Serret–Frenet coordinate system. A novel risk model has been developed to depict the association between USVs and obstacles based on SFC. Combined with an artificial potential field that accounts for environmental obstacles, model predictive control (MPC) based on state space is employed to achieve the optimal control sequence. The stability of the designed controller is demonstrated by means of the Lyapunov method and zero-pole analysis. Through simulation, it has been demonstrated that the controller is asymptotically stable concerning track error deviation, heading angle deviation, and heading angle speed, and its good stability and robustness in the presence of multiple risks are verified.
Keywords: USVs; model predictive control; obstacle avoidance; trajectory tacking; risk augmentation USVs; model predictive control; obstacle avoidance; trajectory tacking; risk augmentation

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MDPI and ACS Style

Li, W.; Zhang, J.; Wang, F.; Zhou, H. Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking. J. Mar. Sci. Eng. 2023, 11, 2283. https://doi.org/10.3390/jmse11122283

AMA Style

Li W, Zhang J, Wang F, Zhou H. Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking. Journal of Marine Science and Engineering. 2023; 11(12):2283. https://doi.org/10.3390/jmse11122283

Chicago/Turabian Style

Li, Wei, Jun Zhang, Fang Wang, and Hanyun Zhou. 2023. "Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking" Journal of Marine Science and Engineering 11, no. 12: 2283. https://doi.org/10.3390/jmse11122283

APA Style

Li, W., Zhang, J., Wang, F., & Zhou, H. (2023). Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking. Journal of Marine Science and Engineering, 11(12), 2283. https://doi.org/10.3390/jmse11122283

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