Model-free adaptive control for unmanned surface vessels: a literature review
Yujuan Wang, Chao Shen, Jiahui Huang, Hua Chen
Abstract
Model-Free Adaptive Control (MFAC) is a control strategy that eliminates the need for prior knowledge of the system model by leveraging online data to learn the system dynamics and design controllers. This paper offers a comprehensive exploration of the significance of control theory in unmanned surface vessels (USVs), with a particular focus on data-driven approaches. It provides a comprehensive overview of various MFAC algorithms proposed for USVs in diverse scenarios, including neural network-based MFAC, reinforcement learning-based MFAC, and fuzzy logic-based MFAC. The objective of this review is to provide a profound understanding of the latest advancements in MFAC technologies and serve as a guiding resource for further developments in the field.