Abstract:
Derivation of mathematical model from any dynamic system has a vital role in
design, analysis and simulation. Higher order complex models are obtained from
physical systems. Partial differential equations (PDEs) and Ordinary differential
equations (ODEs) are used to represent these complex models. As these models
should be simplify for the ease in solution, reduced order models (ROMs) are
needed which approximates with the actual system as much close as possible. In
the area of model order reduction (MOR), considerable amount of work has been
carried out. Existing techniques show large approximation error, error bound and
also have stability issues. In this thesis, a new frequency weighted MOR technique
is proposed for continuous time systems. The proposed technique guarantees in
reduced order system (ROS) and yield low approximation error in the presence of
double sided weightings. The significance of proposed technique is intimated by
numerical problems and comparison with the existing frequency MOR techniques.
Practical applications of MOR are:
• Real time applications in industry
• In missile system analysis
• Image compression
