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Trajectory tracking control for underactuated unmanned surface vehicle subject to uncertain dynamics and input saturation

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Abstract

In this paper, one concerns with the problem of trajectory tracking control for an underactuated unmanned surface vehicle subject to uncertain dynamics and input saturation. A first-order sliding surface and a second-order sliding surface are hired to design surge control law and yaw control law, respectively, which together form an underactuated trajectory tracking controller. Furthermore, the potential input saturation problem is solved through an auxiliary design system. Neural shunting model is introduced into the design of the controller to avoid the increase in calculation caused by variable derivation. The minimum learning parameter method of neural network replaces the traditional multilayer neural network to compensate uncertain dynamics and time-varying disturbances, which further reduces the computational burden of the controller. Besides, two adaptive robust terms are introduced to further enhance the robustness of the trajectory tracking system. Finally, comparative simulation experiments are carried out to verify the universality and superiority of the trajectory tracking control strategy.

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Acknowledgements

The authors would like to thank the reviewers for their constructive comments, which have improved the quality of this paper. This work was supported in part by National Natural Science Foundation of China under Grant 51609033, Natural Science Foundation of Liaoning Province under Grant 20180520005, the Key Development Guidance Program of Liaoning Province of China under Grant 2019JH8/10100100, the Soft Science Research Program of Dalian City of China under Grant 2019J11CY014 and Fundamental Research Funds for the Central Universities under Grant 3132021106, 3132019005, 3132019311.

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Correspondence to Dongdong Mu.

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Mu, D., Wang, G. & Fan, Y. Trajectory tracking control for underactuated unmanned surface vehicle subject to uncertain dynamics and input saturation. Neural Comput & Applic 33, 12777–12789 (2021). https://doi.org/10.1007/s00521-021-05922-x

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  • DOI: https://doi.org/10.1007/s00521-021-05922-x

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