The paper presents an adapted least squares identification method for reduced-order parametric models. On the example of the open velocity loop, different model approaches were implemented in a motion control system. Furthermore, it is demonstrated how the accuracy of the method can be improved. Finally, experimental results are shown.
In this study, a procedure for optimal selection of measurement points using the D-optimality criterion to find the best calibration curves of measurement sensors is proposed. The coefficients of calibration curve are evaluated by applying the classical Least Squares Method (LSM). As an example, the problem of optimal selection for standard pressure setters when calibrating a differential pressure sensor is solved. The values obtained from the D-optimum measurement points for calibration of the differential pressure sensor are compared with those from actual experiments. Comparison of the calibration errors corresponding to the D-optimal, A-optimal and Equidistant calibration curves is done.