Genetic algorithm in parameter estimation of nonlinear dynamic systems

Abstract

We introduce a multi-model parameter estimation method for nonlinear dynamic systems. The method employs a genetic search with a recursive probability selection mechanism for parameter estimation. The method is applied to nonlinear systems with known structure and unknown parameters. A new technique is used to determine the selection probabilities. First, a population of models with random parameter vectors is produced. Second, a probability is recursively assigned to each member of a generation of models. The probabilities reflect the closeness of each model output to the true system output. The probabilities have to satisfy an entropy criterion so as to enable the genetic algorithm to avoid poor solutions. This is a new feature that enhances the performance of the GA on the parameter estimation problem. Finally, the probabilities are used to create a new generation of models by the genetic algorithm. Numerical simulations are given concerning the parameter estimation of a planar robotic manipulator.