A Cascaded Genetic Algorithm with Adaptive Length of the Chromosome for Blind System Order and Parameters Identification

Abstract

Blind system order and parameters identification are the challenging problems in robotics and automation engineering. In this paper, a blind system order and parameters identification algorithm based on a cascaded genetic algorithm for linear time-invariant systems is proposed. It estimates both the order and parameters of the system. The main features of this work include the adaptive length of the chromosome for ascending system order identification, no limitation of the order of the system to approximate, robust to the system noise, and capable of providing low order approximation to the system. In addition, no overestimation of the system order will be occurred by the proposed method. The experimental result reveals that the proposed method approximates the order and parameters of the black box system correctly and robust to the system noise.