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Adaptive Anti-noise Least-Squares Algorithm for Parameter Identification of Unmanned Marine Vehicles: Theory, Simulation, and Experiment

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Abstract

In this paper, an adaptive anti-noise least-squares algorithm (ANLS) is proposed for parameter identification of an unmanned marine vehicle in the presence of measurement noise. As a basis, a horizontal-plane second-order nonlinear Nomoto model is established and transformed into a discrete-time model for parameter identification. Then, a noise reduction term is added to the loss function to achieve a trade-off between the anti-noise effect and parameter identification accuracy. Furthermore, the Levenberg–Marquardt algorithm is embedded into the parameter identification algorithm to achieve adaptive coefficient optimization. Finally, the simulation and experimental data are utilized for parameter identification and performance validation. By comparing with the recursive least-squares algorithm and least-squares support vector machine algorithm, the excellent anti-noise and maneuvering prediction abilities of the proposed ANLS algorithm are verified, i.e., up to 84% reduction of the identification error in the simulation and less than \(4^\circ\) of the heading angle prediction error in the experiment.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 51909161, in part by the Natural Science Foundation of Shanghai under Grant 22ZR1434600, in part by the Shanghai Sailing Program under Grant 19YF1424100, in part by the Open Research Fund of Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources under Grant MESTA-2020-B008, and in part by the Key Prospective Research Fund of Shanghai Jiao Tong University under Grant 2020QY10.

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Authors and Affiliations

  1. School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200030, China

    Yiming Zhong, Caoyang Yu, Rui Wang, Tianqi Pei & Lian Lian

  2. Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou, China

    Caoyang Yu

  3. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Lian Lian

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  1. Yiming Zhong
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Correspondence to Caoyang Yu.

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Zhong, Y., Yu, C., Wang, R. et al. Adaptive Anti-noise Least-Squares Algorithm for Parameter Identification of Unmanned Marine Vehicles: Theory, Simulation, and Experiment. Int. J. Fuzzy Syst. 25, 369–381 (2023). https://doi.org/10.1007/s40815-022-01424-7

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