MODEL ORDER REDUCTION IN TIME DOMAIN

Abstract

Most of the physical systems can be represented by mathematical models. The mathematical procedure of system modeling often leads to a comprehensive description of a process in the form of high-order differential equations which are difficult to use either for analysis or for controller synthesis. It is. therefore, useful and sometimes necessary to find the possibility of some equations of the same type but of lower order that may be considered to adequately reflect almost all essential characteristics of the system under consideration. In this dissertation four different methods of obtaining reduced order models are explained, as minimal realization method which is based on the markov parameters and system should be non-singular. Sometimes this method generate unstable reduce order system even when high order system is stable. The second method which are discussed are Hermite normal form using hankel matrix. Next method is Aggregation by continued fraction expansion, which is based on a Taylor series expansion of the system's closed loop transfer function about s = 0. The fourth method is Singular perturbation method, which is used to find the reduced order model of a decentralized control system. It decouple the system into slow and fast subsystem. Fast subsystem eliminate by this method while slow subsystem are preserved.