Ship docking and undocking control with adaptive-mutation beetle swarm prediction algorithm

被引:13
|
作者
Wang, Le [1 ]
Li, Shijie [1 ]
Liu, Jialun [2 ,3 ]
Wu, Qing [1 ]
Negenborn, Rudy R. R. [2 ,4 ]
机构
[1] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan 430063, Peoples R China
[2] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China
[3] Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Peoples R China
[4] Delft Univ Technol, Dept Maritime & Transport Technol, NL 2628 CD Delft, Netherlands
基金
中国国家自然科学基金;
关键词
Ship; Docking; Undocking; Intelligent optimization algorithm; Predictive control; OPTIMIZATION;
D O I
10.1016/j.oceaneng.2022.111021
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Autonomous docking and undocking control is an important part of intelligent ship motion control. In this study, the adaptive-mutation beetle swarm prediction (AMBS-P) algorithm is used to propose a control approach for autonomous docking and undocking. Firstly, this paper introduces the principle of the AMBSP algorithm, then the convergence is proved. Secondly, the "Tito-Neri"model ship is introduced as a case study, and the thrust allocation process is described. Finally, the effect of docking and undocking is verified in multiple scenarios starting from different angles. In the verification, first of all, when designing the docking and undocking controllers, the correctness of the algorithm and the practicality of the control are verified by whether there is ship drag or not. Secondly, by analyzing the parameters of the algorithm, the optimal parameters of it are determined and verified in the real environment. Thirdly, compared with typical proportion-integral-derivative (PID) algorithm and nonlinear model predictive control (NMPC) algorithm, the AMBS-P algorithm has better results for autonomous docking and undocking control, no matter in long-distance or short-distance. The research shows that the AMBS-P algorithm has a fast response and good effect for the ship autonomous docking and undocking, and does not rely too much on the system model.
引用
收藏
页数:22
相关论文
共 50 条
  • [21] An adaptive mutation strategy for differential evolution algorithm based on particle swarm optimization
    Abhishek Dixit
    Ashish Mani
    Rohit Bansal
    Evolutionary Intelligence, 2022, 15 : 1571 - 1585
  • [22] An Improved Chicken Swarm Optimization Algorithm Based on Adaptive Mutation Learning Strategy
    Zhou, Xin-Xin
    Gao, Zhi-Rui
    Yi, Xue-Ting
    Journal of Computers (Taiwan), 2022, 33 (06) : 1 - 19
  • [23] Particle swarm algorithm with adaptive mutation based on q-Gaussian distribution
    Zhao, Wei
    San, Ye
    Shi, Hui-Shu
    Shenyang Gongye Daxue Xuebao/Journal of Shenyang University of Technology, 2012, 34 (03): : 354 - 360
  • [24] An adaptive mutation strategy for differential evolution algorithm based on particle swarm optimization
    Dixit, Abhishek
    Mani, Ashish
    Bansal, Rohit
    EVOLUTIONARY INTELLIGENCE, 2022, 15 (03) : 1571 - 1585
  • [25] Adaptive inverse control based on particle swarm optimization algorithm
    Wang, YuShen
    Wang, Kejun
    Qu, JiaSheng
    Yang, YuRong
    2005 IEEE International Conference on Mechatronics and Automations, Vols 1-4, Conference Proceedings, 2005, : 2169 - 2172
  • [26] Auto Pilot Ship Heading Angle Control Using Adaptive Control Algorithm
    Hussain, Abadal-Salam T.
    Hazry, D.
    Ahmed, S. Faiz
    Alward, Wail A. A.
    Razlan, Zuradzman M.
    Taha, Taha A.
    ICAROB 2017: PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS, 2017, : P731 - P734
  • [27] Prediction and Suppression of Twisted-wire Pair Crosstalk Based on Beetle Swarm Optimization Algorithm
    Zhou, Jianming
    Li, Shijin
    Zhang, Wu
    Yan, Wei
    Zhao, Yang
    Ji, Yanxing
    Liu, Xingfa
    APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL, 2021, 36 (04): : 435 - 441
  • [28] An Improved Beetle Swarm Optimization Algorithm for the Intelligent Navigation Control of Autonomous Sailing Robots
    Zhou, Lin
    Chen, Kai
    Dong, Hang
    Chi, Shukai
    Chen, Zhen
    IEEE ACCESS, 2021, 9 : 5296 - 5311
  • [29] Improved beetle swarm optimization algorithm based PID neural network for decoupling control
    Ding, Jie
    Wu, Min
    Ma, Zhibao
    2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, : 5299 - 5304
  • [30] Parameters Selection of LSSVM Based on Adaptive Genetic Algorithm for Ship Rolling Prediction
    Wang Yuchao
    Fu Huixuan
    2014 33RD CHINESE CONTROL CONFERENCE (CCC), 2014, : 6632 - 6636