Model reduction in physical domain

Abstract

Modeling is an essential part of the analysis and the design of dynamic systems. Contemporary computer algorithms can produce very detailed models for complex systems with little time and effort. However, over complicated models may not be efficient. Therefore, reducing a model to a more manageable size has become an attractive research topic. A very useful type of reduced models is obtained by removing as many physical components as possible from the original model. Such approach is known as model reduction in the physical domain. Many results have been achieved in model reduction in the physical domain during past decades. Nonetheless, the newest developments in engineering practice as well as in theoretical research have brought about further challenges and opportunities. In this thesis, the criteria and the scope of model reduction in the physical domain are reinvestigated. As a result, a criterion based on the Hc norm of certain error model is proposed. Furthermore, the scope of model reduction is also extended. In this thesis, a mathematical framework is constructed for model reduction in physical domain. Specifically, model reduction problem is formulated as an optimization problem with bilinear matrix inequality (BMI) constraints. A branch-and-bound algorithm is developed to solve the BMI problem. The algorithm is proved to converge to global optimum. Several examples are presented to illustrate the use of the proposed model reduction scheme.