Fuzzy controller for AUV robots based on machine learning and genetic algorithm

Abstract

Marine robots play a crucial role in exploring and investigating underwater and seafloor environments, organisms, structures, and resources. In this study, we developed a control system for a small marine robot and conducted simulation experiments to evaluate its performance. The control system is based on fuzzy control, which resembles human control by defining rules, quantifying them through membership functions, and determining the appropriate manipulation level. Moreover, a genetic algorithm was employed to optimize the coefficients of a function utilized by the proposed controller in the non-fuzzification process to establish the operating parameters. When implementing this control system during simulations, the marine robot successfully reached a desired position within a specified time frame.