Robust controller design for course changing / course keeping control of a ship using PSO enabled automated quantitative feedback theory

Abstract

This paper presents the design of a robust course controller for a cargo ship interacting with an uncertain environment using particle swarm optimization (PSO) enabled automated quantitative feedback theory. The plant model considers here is Nomotopsilas second order model, with structure parametric variation. In the present paper we have taken Nomotopsilas second order model as it is valid for high frequencies also, while first order model is restricted to low frequencies. In the present paper, the automated PSO enabled QFT design method is used to synthesize a robust course controller that can undertake the exact amount of plant uncertainty and can ensure a proper trade off between robust stability specifications and tracking performance over the entire range of frequencies. The present work is the continuation of the work done by the first author where controller is synthesized manually with the consideration of same process model. But in this article the PSO technique has been employed to tune the controller automatically that can greatly reduces the computational effort compared to manual graphical techniques. It has also been demonstrated that this methodology not only automates loop-shaping but also improves design quality and, most usefully, improves the quality with a reduced order controller.