System identification and linearisation using genetic algorithms with simulated annealing

Abstract

This paper develops high performance system identification and linearisation techniques, using a genetic algorithm. The algorithm is fine tuned by simulated annealing, which yelds a faster convergence and a more accurate search. This global search technique is used to identify the parameters of a system described by an ARMAX model in the presence of white noise and to approximate a nonlinear multivariable system by a linear time-invariant state-space model. Results obtained show that simple step inputs can be used for effective system identification and linearisation with much higher performance than is possible by conventional means.