Identification of Continuous-time Linear Time-varying Systems with Abrupt Changes in Parameters

Abstract

Abstract In this paper, we propose an adaptive identification method for continuous-time systems with slowly time-varying parameters subject to infrequent abrupt changes using sampled data in the presence of measurement noise. The proposed estimator consists of two steps. First, the time-derivatives of the input and output signals are approximated through a combination of a Kalman filter and fixed-interval smoother. The parameters of the time-varying system are then estimated using the approximate time-derivatives by two Kalman filters running forward and backward in time to track the abrupt changes. The performance of the proposed estimator is verified through simulation studies where it is shown to perform better than an existing direct continuous-time estimator.