Identification of continuous-time systems with unknown time delays by global nonlinear least-squares method

Abstract

This paper considers the identification problem of continuous-time systems with unknown time delays from sampled input-output data. By using a digital prefilter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delays need not be an integral multiple of sampling period. Then an iterative separable nonlinear least-squares (SEPNLS) method which estimates the time delays and transfer function parameters separably is derived. Futhermore, we propose an iterative global SEPNLS method to avoid convergence to a local minimum of the SEPNLS method by using of stochastic global-optimization techniques. Simulational results show that the global SEPNLS method is able to converge to global estimate.