Fractional-Order Controller for Course-Keeping of Underactuated Surface Vessels Based on Frequency Domain Specification and Improved Particle Swarm Optimization Algorithm

被引:80
作者
Li, Guangyu [1 ]
Li, Yanxin [1 ]
Chen, Huayue [2 ]
Deng, Wu [3 ]
机构
[1] Dalian Jiaotong Univ, Sch Software, Dalian 116026, Peoples R China
[2] China West Normal Univ, Sch Comp Sci, Nanchong 637002, Peoples R China
[3] Civil Aviat Univ China, Sch Elect Informat & Automat, Tianjin 300300, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 06期
关键词
underactuated surface vessels; fractional order (PID mu)-D-lambda controller; course-keeping; improved particle swarm optimization algorithm; autopilot; FAULT-DIAGNOSIS; ROBUST-CONTROL; TRACKING;
D O I
10.3390/app12063139
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In this paper, a new fractional-order (FO) (PID mu)-D-lambda controller is designed with the desired gain and phase margin for the automatic rudder of underactuated surface vessels (USVs). The integral order lambda and the differential order mu are introduced in the controller, and the two additional adjustable factors make the FO (PID mu)-D-lambda controller have better accuracy and robustness. Simulations are carried out for comparison with a ship's digital PID autopilot. The results show that the FO (PID mu)-D-lambda controller has the advantages of a small overshoot, short adjustment time, and precise control. Due to the uncertainty of the model parameters of USVs and two extra parameters, it is difficult to compute the parameters of an FO (PID mu)-D-lambda controller. Secondly, this paper proposes a novel particle swarm optimization (PSO) algorithm for dynamic adjustment of the FO (PID mu)-D-lambda controller parameters. By dynamically changing the learning factor, the particles carefully search in their own neighborhoods at the early stage of the algorithm to prevent them from missing the global optimum and converging on the local optimum, while at the later stage of evolution, the particles converge on the global optimal solution quickly and accurately to speed up PSO convergence. Finally, comparative experiments of four different controllers under different sailing conditions are carried out, and the results show that the FO (PID mu)-D-lambda controller based on the IPSO algorithm has the advantages of a small overshoot, short adjustment time, precise control, and strong anti-disturbance control.
引用
收藏
页数:19
相关论文
共 60 条
[1]   Optimal design of Fractional order PID controller based Automatic voltage regulator system using gradient-based optimization algorithm [J].
Altbawi S.M.A. ;
Mokhtar A.S.B. ;
Jumani T.A. ;
Khan I. ;
Hamadneh N.N. ;
Khan A. .
Journal of King Saud University - Engineering Sciences, 2024, 36 (01) :32-44
[2]   Using selection to improve particle swarm optimization [J].
Angeline, PJ .
1998 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION - PROCEEDINGS, 1998, :84-89
[3]  
[Anonymous], 2002, The Fractional Calculus: Theory and Applications of Differentiation and Integration to Aribitrary Order
[4]   Fractional PID controllers tuned by evolutionary algorithms for robot trajectory control [J].
Bingul, Zafer ;
Karahan, Oguzhan .
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2012, 20 :1123-1136
[5]   Self-Mutual Information-Based Band Selection for Hyperspectral Image Classification [J].
Chang, Chein-, I ;
Kuo, Yi-Mei ;
Chen, Shuhan ;
Liang, Chia-Chen ;
Ma, Kenneth Yeonkong ;
Hu, Peter Fuming .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (07) :5979-5997
[6]   An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine [J].
Chen, Huiling ;
Zhang, Qian ;
Luo, Jie ;
Xu, Yueting ;
Zhang, Xiaoqin .
APPLIED SOFT COMPUTING, 2020, 86
[7]   Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD [J].
Cui, Hongjiang ;
Guan, Ying ;
Chen, Huayue .
IEEE ACCESS, 2021, 9 :120297-120308
[8]   An Enhanced MSIQDE Algorithm With Novel Multiple Strategies for Global Optimization Problems [J].
Deng, Wu ;
Xu, Junjie ;
Gao, Xiao-Zhi ;
Zhao, Huimin .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 52 (03) :1578-1587
[9]   A Novel Gate Resource Allocation Method Using Improved PSO-Based QEA [J].
Deng, Wu ;
Xu, Junjie ;
Zhao, Huimin ;
Song, Yingjie .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (03) :1737-1745
[10]   Compound Fault Diagnosis Using Optimized MCKD and Sparse Representation for Rolling Bearings [J].
Deng, Wu ;
Li, Zhongxian ;
Li, Xinyan ;
Chen, Huayue ;
Zhao, Huimin .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71