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

• Published in: International journal of control, automation, and systems 2022, Automation, and Systems, 20(3), , pp.842-856
• Main Authors: Ren, Jia, Chen, Zengqiang, Yang, Yikang, Sun, Mingwei, Sun, Qinglin, Wang, Zenghui
• Format: Article
• Language: English
• Subjects: Active control; Control; Engineering; Machine learning; Marine environment; Mechatronics; Optimization; Parameters; Particle swarm optimization; Perturbation; Regular Papers; Rejection; Robotics; Tuning; White noise; 제어계측공학
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-021-0062-8

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.