Identification of time-varying delay systems using particle swarm optimization

Abstract

Particle swarm optimization algorithm is a new evolutionary computation method, which is applicable to complex optimization problems that are nonlinear, nondifferentiable and multimodal. A method for identification of time-varying delay systems using particle swarm optimization is proposed. The basic idea of the method is that the identification problems are cast as mixed-integer nonlinear programming problems, and then particle swarm optimization algorithm is used to find the optimal estimation of the time-varying parameters. Simulation results reveal that the suggested identification scheme possesses a good tracking ability to the time-varying delay systems.