MODEL ORDER REDUCTION TECHNIQUES IN CONTROL SYSTEM DESIGN

In modeling physical systems, the order of the system gives an idea of the measure of accuracy of the modeling of the system. The higher order system model will be more accurate in describing the physical system. But in several cases, the amount of information contained in a complex model may obfuscate simple, insightful behaviors, which can be better captured and explored by a model with a much lesser order. Thus, by approximating a higher order system to a suitable lower order system, a much better understanding of the system is reached. Hence, the process of Model Order Reduction involves studying the properties of a complex dynamic system in application for reducing its complexity, while preserving (to the maximum possible extent) its input – output behavior. Depending upon the practical application, certain specific properties of the higher order complex system have to be preserved in the reduced order model.
The detailed comparative analysis of proposed model order reduction scenarios over the conventional reduction schemes has been presented in this book to validate the effectiveness of the proposed reduction schemes. MATLAB simulation has been performed for different kind of linear time invariant systems and the results show the superiority of the proposed scenarios. The proposed model order reduction methods are applied to the design on discrete and continuous controllers, suboptimal controller and design of IIR filter.