Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Abstract

Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies.

Identification of Robot Joint Torsional Stiffness Based on the Amplitude of the Frequency Response of Asynchronous Data

“…Therefore, The identification accuracy depends heavily on the excitation signal, which should cover the frequency components contained in the system. The most used excitation signals are swept-frequency signals and PRBS [20,21]. The excitation signal is superimposed on the torque reference obtained from the speed controller as the input signal to excite the system.…”