Autonomous Underwater Vehicle Path Tracking Based on the Optimal Fuzzy Controller with Multiple Performance Indexes

Autonomous underwater vehicles (AUVs) are increasingly being used in missions involving submarine cable detection, underwater archaeology, pipeline inspection, military reconnaissance, and so on. It is very important to realize AUV path tracking to accomplish these missions. In this paper, a fuzzy controller based on the established kinematic and dynamic models of AUV systems is presented to solve the AUV path-tracking problem. In order to design the fuzzy controller to exhibit good performance, we select the path length, smoothness, and cross-track position error as the multiple optimization performance indexes for the fuzzy controller. We propose the particle swarm optimization (PSO) algorithm to determine the parameters of the membership functions. Different scenarios are presented to test the performance of the proposed algorithm, including the straight line, sine curve, half-moon shape, Archimedean spiral, and practical paths. The results are given to illustrate the effectiveness and feasibility of the fuzzy controller with the optimization of multiple performance indexes.

Automated summary

This paper presents a fuzzy controller based on the established kinematic and dynamic models of autonomous underwater vehicles (AUVs) to solve the AUV path-tracking problem. Multiple optimization performance indexes, including path length, smoothness, and cross-track position error, are selected to design the fuzzy controller for good performance. The particle swarm optimization (PSO) algorithm is proposed to determine the parameters of the membership functions. Different scenarios are tested to demonstrate the effectiveness and feasibility of the fuzzy controller with the optimization of multiple performance indexes.