Application of genetic algorithms to system identification

System identification is a pre-requisite to the analysis of a dynamic system and design of an appropriate controller for improving its performance. In conventional identification methods, a model structure is selected and the parameters of that model are calculated by optimising an objective function. This process usually requires a large set of input/output data from the system which is not always available. In addition the obtained parameters may be only locally optimal. In this work genetic algorithms are applied to system identification. A system is assumed to have an ARMAX model, the parameters of which are obtained using the search process of the genetic algorithms. The method developed is presented and results of its application to a number of experimental systems are described. The results obtained are quite encouraging.