Identification of single-DOF motion control systems via filtered linear regression

Abstract

This paper proposes a new on-line identification method for single-degree-of-freedom (DOF) motion control systems. The proposed method is based on the application of the well-known least mean squares (LMS) methods to their filtered linear regression models. As a result, its implementation requires neither the information of acceleration nor high-pass filtering of velocity, in contrast with the direct application of the LMS methods to on-line identification of single-DOF motion control systems. Most importantly, we show that the existence of steady-state oscillation can assure the persistent excitation (PE) property for parameter convergence. As a matter of fact, in practical applications, the existence of steady-state oscillation can be easily guaranteed by periodic excitation. The generality and practical use of the proposed method are demonstrated through some simulation results.