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Single-parameter-learning-based robust adaptive control of dynamic positioning ships considering thruster system dynamics in the input saturation state

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Abstract

The focus of this paper is presented on robust adaptive dynamic positioning control in the face of thruster system dynamics. In the maritime domain, it considers the issues of model parameter ingestion, unknown time-varying disturbances, and input saturation. First, a finite-time convergent disturbance observer is used for the online estimation of unknown time-variant disturbances. Additionally, the model ingestion problem is also solved with a single-parameter learning neural network. Furthermore, a robust control term is introduced to account for undesired errors. Then, the thruster dynamics equation is considered to solve the issue of thruster dynamics characteristics in the designed process of the controller. Finally, the input saturation problem is addressed with a finite-time auxiliary dynamic system. The suggested dynamic positioning control approach allows the ship to retain the required position and direction, as demonstrated. Respectively, all control variables in the dynamic positioning control system are consistent and ultimately bounded. At last, the proposed dynamic positioning control method was validated through the experimental simulations on the supply vessel Northern Clipper.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 51609033, the 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, Fundamental Research Funds for the Central Universities under Grant 3132021106, 3132019005, 3132019311 and China Postdoctoral Science Foundation 2022M710569.

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Authors and Affiliations

  1. College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, Liaoning, China

    Dongdong Mu, Yupei Feng, Guofeng Wang, Yunsheng Fan & Yongsheng Zhao

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  1. Dongdong Mu
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  2. Yupei Feng
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  3. Guofeng Wang
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  4. Yunsheng Fan
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  5. Yongsheng Zhao
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Correspondence to Yupei Feng.

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Mu, D., Feng, Y., Wang, G. et al. Single-parameter-learning-based robust adaptive control of dynamic positioning ships considering thruster system dynamics in the input saturation state. Nonlinear Dyn 110, 395–412 (2022). https://doi.org/10.1007/s11071-022-07657-3

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