Separable identification of continuous-time systems having multiple unknown time delays from sampled data

Abstract

This paper treats the problem of continuous-time model identification with unknown time delays from sampled data. The proposed method estimates the plant and the time delays in a separable way. More precisely, the plant is estimated by the standard recursive Least Square algorithm while the time delays are explicitly estimated by the Gauss-Newton algorithm. This means clear separation between the system dynamics and time delays. Numerical simulations are given at last to illustrate the validity of the proposed scheme.