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A proper real-time system identification method is of great importance in order to acquire an analytical model that sufficiently represents the characteristics of the monitored system. While the use of different time-domain online identification techniques has been widely recognized as a powerful approach to system diagnostics, the frequency-domain identification techniques have primarily been considered for offline commissioning purposes. This paper addresses issues in the online frequency-domain identification of a mechanical system with varying dynamics; particular attention is paid to detect the changes in the system dynamics. A closed-loop online identification method is presented that is based on a sliding discrete Fourier transform at a selected set of frequencies. The method is experimentally validated by a closed-loop controlled servomechanism with a limited stroke and time-varying parameters. [ABSTRACT FROM PUBLISHER]

Many control schemes rely on an analytical model of the servomechanism to be controlled, and hence, accurate knowledge about the position- and time-dependent parameter variability becomes crucial in many contexts, such as robust control methods. Although a properly designed robust controller can cope with a large parameter variation, real-time identification of the system parameter behavior could lead to several advantages by means of monitoring the varying dynamics, e.g., the predetermined uncertainty region around the nominal value. This paper addresses issues in online parameter estimation of a linear tooth belt drive with a limited stroke. Particular attention is paid to detecting the position-dependent changes in the system dynamics by using recursive least squares algorithm and exciting the system in different cart positions in order to identify the varying dynamics. The algorithm used is based on an indirect output-error identification scheme. The experimentally estimated parameters are compared with the corresponding two-mass model parameters. The results show an acceptable agreement and demonstrate the feasibility of the estimation method to estimate the parameters of a closed-loop controlled servomechanism with a limited stroke and time-varying parameters. [ABSTRACT FROM PUBLISHER]

Accurate and updated knowledge of the electric machine parameters provides benefits in many applications, such as model-based control methods. This paper presents an online parameter estimation method for double-star interior permanent-magnet (IPM) machines supplied with voltage-source inverters (VSI). The estimation method is based on a decoupled D–Q model of the machine and a recursive least-squares (RLS) algorithm. Initial machine parameters are estimated at a standstill. Then, in the rotating operating state, the inductances and the flux linkage produced by the PMs are updated. The estimation in the operating state is performed with a flux-linkage-based approach. The experimental results are compared with the corresponding values obtained from finite-element analyses. The results show an acceptable agreement and demonstrate the feasibility of the estimation scheme to estimate the parameters of double-star PM machines. The parameters are validated by comparing experimental and simulated open-loop responses and steady-state torques. [ABSTRACT FROM PUBLISHER]