Online Estimation of Linear Tooth Belt Drive System Parameters

Abstract

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.