High Fidelity Reduced Order System Modeling Optimization And Its Application

Abstract

Complexity of any physical system has always been a major concern for newlineresearchers. Complexity of any system creates difficulty in performing exact analysis. newlineAlso, it makes the task undesirable and tedious due to the involvement of computational newlineand economic considerations. In order to carry the analysis and synthesis of such complex newlinephysical systems, first a mathematical model has to be developed that can substitute and newlinereal time system. The quest for mathematical model is legitimated because the practice of newlinesuch models expedites the analysis of systems they describe. In many practical situations a newlinequite complex and high order model is obtained in modeling various components and newlinesubsystems of a system. Depending on whether a system can be translated in terms of newlinesingle high order differential equation or in the form of a set of first order differential newlineequations, the order of the system is decided by the order of the single differential equation newlineor the number of first order differential equations used in the model as per the case. newlineIn many references of control and system theory, it is effective and necessary to newlineallude to a simplified model of complex process both for simulation and control system newlinesynthesis. One of such methods of simplification is Model Order Reduction (MOR) i.e. newlineobtaining a low order model for a high order complex system such that the primary newlinecharacteristics remain intact in terms of response. Some of the other motivations for newlinedeveloping reduced order models are as follows: newlineand#61623; To reduce computational complexity newlineand#61623; To reduced hardware complexity newlineand#61623; To improve understating of the system newlineand#61623; To reduced cost and time consumption newlineand#61623; To produce feasible controller design newline