A self-learning ship steering controller based on adaptive Critic Designs

Abstract

This paper addresses the problem of generating autonomously an optimal control action sequence for ship steering control based on Adaptive Critic Designs. The principal objective is to autonomously design an optimal controller that steers the center of the ship through a number of gates in a particular order using a minimum amount of time. In general, the steering of mobile vehicles depend on the interactions between a vehicle and its supporting medium. Nautical ships present particularly longtime delays in response to rudder movements (control actions). Planning for the future encounters with gates should be part of the current control decision, since the ship's position and orientation as it moves through one gate greatly affect the ease of navigation through successive gates. The proposed adaptive critic design-based controller learns to guide the ship through a set of gates autonomously. The simulation results show the performance of the proposed approach.