Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles

• Published in: International journal of fuzzy systems Vol. 20; no. 2; pp. 572 - 586
• Main Authors: Xiang, Xianbo, Yu, Caoyang, Lapierre, Lionel, Zhang, Jialei, Zhang, Qin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2018; Springer Nature B.V; Taiwan Fuzzy Systems Association
• Subjects: Adaptive control; Artificial Intelligence; Autonomous underwater vehicles; Autonomous vehicles; Computational Intelligence; Computer Science; Controllers; Design; Engineering; Fuzzy control; Fuzzy logic; Fuzzy sets; Management Science; Operations Research; Proportional integral derivative; Robot control; Robotic vehicles; Robotics; Self tuning; Sliding mode control; Surface vehicles; Trajectory planning; Underwater vehicles; Remotely operated vehicles; Fuzzy logic control; Autonomous underwater vehicles; Guidance and control; Unmanned surface vehicles
• ISSN: 1562-2479; 2199-3211
• DOI: 10.1007/s40815-017-0401-3

Fuzzy logic control, due to its simple control structure, easy and cost-effective design, has been successfully employed to the application of guidance and control in robotic fields. This paper aims to review fuzzy-logic-based guidance and control in an important branch of robots—marine robotic vehicles. First, guidance and motion forms including the maneuvering, path following, trajectory tracking, and position stabilization are described. Subsequently, the application of three major classes of fuzzy logic control, including the conventional fuzzy control (Mamdani fuzzy control and Takagi–Sugeno–Kang fuzzy control), adaptive fuzzy control (self-tuning fuzzy control and direct/indirect adaptive fuzzy control), and hybrid fuzzy control (fuzzy PID control, fuzzy sliding mode control, and neuro-fuzzy control) are presented. In particular, we summarize the design and analysis process of direct/indirect adaptive fuzzy control and fuzzy PID control in marine robotic fields. In addition, two comparative results between hybrid fuzzy control and the corresponding single control are provided to illustrate the superiority of hybrid fuzzy control. Finally, trends of the fuzzy future in marine robotic vehicles are concluded based on its state of the art.