ORDER REDUCTION AND CONTROLLER DESIGN USING EVOLUTIONARY TECHNIQUES

Abstract

Recently, evolutionary techniques of genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modem heuristic optimization techniques like ant colony optimization, evolutionary programming etc. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. In this dissertation work evolutionary techniques of PSO and GA optimization are employed for finding stable reduced order models for various single-input- single-output large-scale linear systems and at the same time in further work these two techniques has`been employed for fine tuning or designing of PID controller. Both the techniques guarantee stability of reduced order model if the original high order model is stable. Also the controller designed using this evolutionary techniques has shown performance much better than the conventional controller designing techniques .Both the methods applied on the order reduction are based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Similarly in case of controller designing using these evolutionary techniques, various performance criterions like. ISE, IAE, ITAE and MSE are used. Both the methods are illustrated through numerical example from literature and the results are compared with recently published conventional model reduction techniques and the controller designing techniques.