Implementation of Fuzzy Adaptive Neuronal Control Applied to Trajectory Tracking of an Autonomous Unmanned Surface Vehicle

This article presents the hardware implementation of the fuzzy adaptive neural control law for trajectory tracking based on a simplified model of the navigation system of an autonomous unmanned surface vehicle prototype for water quality monitoring. The parameters of the simplified model are estimated. Trajectory tracking is carried out in-line by fuzzy adaptive neurons for tuning the gains of a Proportional Integral Derivative controller (PID). The stability analysis is developed. Simulations were obtained at Matlab®/Simulink for circular trajectories and the implementation in Arduino Mega boards, very good precision is obtained for the experimental results in a diving pit.