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Grey Wolf Optimization Based Active Disturbance Rejection Control Parameter Tuning for Ship Course

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  • Control Theory and Applications
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Abstract

It is important to control the ship course in complicated ocean environment. In this paper, a Grey Wolf Optimization (GWO) based Active Disturbance Rejection Control (ADRC) tuning method is proposed in the application of the ship course. Here, GWO is used to tune the parameters of ADRC. To validate the performance of the proposed method, some simulations have been carried out and the results are compared with the results of other tuning methods, such as, Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Q-learning and manual tuning. To test the stability of different tuning methods, the cases of no disturbance, constant value disturbance, second-order wave force disturbance, white noise disturbance and mixed disturbance are considered. The robustness of the system for parameters perturbation is analyzed. The research indicates that the GWO based ADRC can achieve better performance than other methods.

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Funding

This work was supported by Natural Science Foundation of China under Grant 61973175, Grant 62073177 and Grant 61973172, Tianjin Research Innovation Project for Postgraduate Students under Grant 2021YJSB018 and Grant 2020YJSB003, South African National Research Foundation under Grant 112108, Grant 132797, Grant 137951, and Grant 112142, South African National Research Foundation Incentive under Grant 114911, and Eskom Tertiary Education Support Programme Grant of South Africa.

Author information

Authors and Affiliations

  1. College of Artificial Intelligence, Nankai University, Tianjin, 300350, China

    Jia Ren, Zengqiang Chen, Yikang Yang, Mingwei Sun & Qinglin Sun

  2. Key Lab of Intelligent Robotics of Tianjin, Tianjin, 300350, China

    Zengqiang Chen

  3. Department of Electrical Engineering, University of South Africa, Pretoria, Florida, 1710, South Africa

    Zenghui Wang

Authors
  1. Jia Ren
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  2. Zengqiang Chen
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  4. Mingwei Sun
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  5. Qinglin Sun
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Corresponding author

Correspondence to Zengqiang Chen.

Additional information

Jia Ren was born in 1996. She received her B.E. degree in automation from the Hebei University of Technology, Tianjin, China, in 2019 and she is currently a Ph.D. student at Nankai University, Tianjin, China.

Zengqiang Chen was born in 1964. He received his B.S., M.E., and Ph.D. degrees from Nankai University, in 1987, 1990, and 1997, respectively. He is currently a professor of control theory and engineering of Nankai University. His current research interests include intelligent predictive control and complex dynamic network.

Yikang Yang received his B.E. degree in Intelligence Science and Technology from Hebei University of Technology, Tianjin, China, in 2017. Currently, he is a Ph.D. student at Nankai University, Tianjin, China. His research interests include electromyography signals processing and machine learning.

Mingwei Sun was born in 1972. He received his Ph.D. degree from the Department of Computer and Systems Science, Nankai University in 2000. He is currently a professor of Nankai University. His research interests include model predictive control, active disturbance rejection control, and nonlinear optimization.

Qinglin Sun received his B.E. and M.E. degrees in Tianjin University, in 1985 and 1990, respectively, and a Ph.D. degree in control science and engineering from Nankai University in 2003. He is currently a professor in Nankai University. His research interests include self-adaptive control and embedded control systems.

Zenghui Wang received his B.Eng. degree in automation from Naval Aviation Engineering Academy, China, in 2002, and a Ph.D. degree in Nankai University in 2007. He is currently a professor with the University of South Africa (UNISA), South Africa. His research interests are control theory and control engineering.

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Ren, J., Chen, Z., Yang, Y. et al. Grey Wolf Optimization Based Active Disturbance Rejection Control Parameter Tuning for Ship Course. Int. J. Control Autom. Syst. 20, 842–856 (2022). https://doi.org/10.1007/s12555-021-0062-8

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  • DOI: https://doi.org/10.1007/s12555-021-0062-8

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