A Global Nonlinear Instrumental Variable Method for Identification of Continuous-Time Systems with Unknown Time Delays

This paper considers the identification problem of continuous-time systems with unknown time delays from sampled input-output data. An iterative global separable nonlinear least-squares (GSEPNLS) method which estimates the time delays and transfer function parameters separably is derived, by using stochastic global-optimization technique to avoid convergence to a local minimum. Futhermore, the GSEPNLS method is modified to a novel global separable nonlinear instrumental variable (GSEPNIV) method to yield consistent estimates if the algorithm converges to the global minimum. Simulational results show that the proposed method works quite well. A GLOBAL NONLINEAR INSTRUMENTAL VARIABLE METHOD FOR IDENTIFICATION OF CONTINUOUS-TIME SYSTEMS WITH UNKNOWN TIME DELAYS Zi-Jiang Yang ∗ Hideto Iemura ∗ Shunshoku Kanae ∗ Kiyoshi Wada ∗ ∗ Department of Electrical and Electronic Systems Engineering, Kyushu University, Hakozaki, Fukuoka 812-8581, Japan TEL: (+81)92-642-3904, FAX: (+81)92-642-3939 E-mail: [email protected] Abstract: This paper considers the identification problem of continuous-time systems with unknown time delays from sampled input-output data. An iterative global separable nonlinear least-squares (GSEPNLS) method which estimates the time delays and transfer function parameters separably is derived, by using stochastic global-optimization technique to avoid convergence to a local minimum. Futhermore, the GSEPNLS method is modified to a novel global separable nonlinear instrumental variable (GSEPNIV) method to yield consistent estimates if the algorithm converges to the global minimum. Simulation results show that the proposed method works quite well. Copyright c ©2005 IFAC This paper considers the identification problem of continuous-time systems with unknown time delays from sampled input-output data. An iterative global separable nonlinear least-squares (GSEPNLS) method which estimates the time delays and transfer function parameters separably is derived, by using stochastic global-optimization technique to avoid convergence to a local minimum. Futhermore, the GSEPNLS method is modified to a novel global separable nonlinear instrumental variable (GSEPNIV) method to yield consistent estimates if the algorithm converges to the global minimum. Simulation results show that the proposed method works quite well. Copyright c ©2005 IFAC