Control system parameter identification using the population based incremental learning (PBIL)

In this paper a technique of how a stochastic search and optimisation technique dubbed the population based incremental learning can be used for parameter identification of continuous control system models, is presented. This method is an abstraction of a simple genetic algorithm (GA) which maintains all the statistical properties of a GA but removes the genetic recombination operators. The method used aims to identify the system parameters from poles and zeros and matches them to the response of the control system signals.