System identification and control using genetic algorithms

Abstract

It is shown how genetic algorithms can be applied for system identification of both continuous and discrete tim systems. It is shown that they are effective in both domain and are able to directly identify physical parameters or pole and zeros. This can be useful because changing one physic parameter might effect every parameter of a system transfer function. The poles and zeros estimates are then used to design a discrete time pole placement adaptive controller. Simulation for minimum and nonminimum phase systems and a system wit unmodeled dynamics are presented.