SYSTEM IDENTIFICATION ALGORITHMS AND APPLICATIONS

Abstract

A fundamental concept in science and technology is that of mathematical modeling. The mathematical model is a mathematical description of dynamic characteristics of a system. The determination of the mathematical model from experimental data is known as system identification. Since the dynamics of real systems are time varying and/or nonlinear in nature, the self-tuning controller design is appropriate where a recursive identification algorithm is utilized to monitor or track the process behavior. The robustness of self tuning is totally dependent upon the robustness of the system identification algorithm and its ability to detect and track rapid changes in system performance or dynamics, that’s why the system identification is introduced in this research. In this dissertation a DC servomotor system has been taken as a case study and its transfer function has been derived. Simulation work and experimental work have been done for the case study. In order to get the simulated system parameters, a program is developed under MATLAB environment. The comparison between the two models is illustrated. In order to get model parameters of the real system, a control trainer (XPO PID) kit is used, and m-file programs was developed to process the data and to estimate the model parameters. Also the comparison between the estimated model parameters of the real data and real data parameters is conducted, and the difference between them is about 16%, this difference referred to system un-modeled dynamics, stochastic noise affecting the experimental work, and other reasons which have been discussed in this dissertation.