On-Line Identification of Continuous Time-Delay Systems Using the Genetic Algorithm

Abstract

This paper proposes a new kind of on-line identification methods of continuous time-delay systems from sampled input-output data. In order to track the time-varying system parameters as well as time-delay, the recursive linear least squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA) which has a high potential for global optimization. The time-delay is coded into binary bit strings and searched by the GA, while the system parameters are updated by the RLS method. Furthermore, this method (GALS method) is hybridized with the sequential nonlinear least squares method to improve the speed of convergence. Simulation results show that both the GALS and hybrid methods are efficient in the case that the system changes abruptly, and among them the hybrid method has superior convergence property to the GALS method and yields excellent estimation results in the case that the system changes with time continuously.