USV course controller optimization based on elitism estimation of distribution algorithm

PID controller is used in most of the course-keeping closed-loop control of Unmanned Surface Vehicle (USV). However, the parameters of PID are difficult to tuning. In this paper, we adopt an elitism estimation of distribution algorithm (EEDA) to optimize the PID, which makes use of the probabilistic model to estimate the optimal solution distribution. It has a better global searching ability. A linear Nomoto model is adopted to simulate the USV, and the PID controller is used to control the course of the USV. The simulation results exhibit the validity of the EEDA.