Identification of Linear Time-Invariant Systems: A Least Squares of Orthogonal Distances Approach

Abstract

This work describes the parameter identification of servo systems using the least squares of orthogonal distances method. The parameter identification problem was reconsidered as data fitting to a plane, which in turn corresponds to a nonlinear minimization problem. Three models of a servo system, having one, two, and three parameters, were experimentally identified using both the classic least squares and the least squares of orthogonal distances. The models with two and three parameters were identified through numerical routines. The servo system model with a single parameter only considered the input gain. In this particular case, the analytical conditions for finding the critical points and for determining the existence of a minimum were presented, and the estimate of the input gain was obtained by solving a simple quadratic equation whose coefficients depended on measured data. The results showed that as opposed to the least squares method, the least squares of orthogonal distances method experimentally produced consistent estimates without regard for the classic persistency-of-excitation condition. Moreover, the parameter estimates of the least squares of orthogonal distances method produced the best tracking performance when they were used to compute a trajectory-tracking controller.

Non-contact online inspection method for the appearance diameter uniformity of chemical fiber filaments

“…Step (b): the sequence diffraction fringe images are divided into multiple sub-regions by the least squares method [21]. Firstly, the diffraction fringe images are converted into a grayscale matrix A H × W , and the matrix is W row H column.…”