Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector

Abstract This article deals with the parameter identification of the generalized time‐varying systems. The time‐varying parameter vector can be expressed as a coefficient matrix multiplied by a measurable disturbance vector, the common identification methods cannot be used to estimate the parameters of the generalized time‐varying systems directly. This motivates us to develop new iterative identification algorithms. The gradient‐based iterative (GI) algorithm is proposed by means of the iterative technique. Moreover, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. The MDW GI algorithm is proposed to improve the parameter estimation accuracy. The numerical simulation is provided and the simulation results show that the proposed identification methods are effective for the generalized time‐varying systems.