000			02462nam a22001817a 4500
003			NUST
005			20260207145030.0
082			_a621.382,LAT
100			_aLatif, Muhammad _932948
245			_aStability Preserving Model Reduction Frameworks for Control of Complex Dynamical Systems / _cMuhammad Latif
260			_aRawalpindi, _bMCS (NUST), _c2025
300			_axxi, 209 p
505			_aIn the domain of control systems engineering, mathematical modeling constitutes the cornerstone for the analysis of dynamical systems, particularly when systems become increasingly complex. However, the computational demands of simulating large-scale systems, incorporating various types of equations, present formidable challenges. Model reduction techniques aim to approximate complex, high-order systems with simpler, lower-order models while retaining acceptable accuracy. These techniques ultimately simplify the design, modeling, and simulation of large-scale systems. This dissertation delves into model reduction techniques tailored for large-scale highdimensional systems, leveraging balanced structures for improving efficiency. Initially, this research carries out literature review of existing model order reduction techniques in order to examine their limitations. Over the past few decades, numerous model order reduction methods have been proposed in the literature. Among these, the balanced truncation method is widely adopted due to its simplicity, ability to preserve stability in reduced models, and incorporation of a priori error bounds. The method involves balancing the original system and subsequently truncating the least controllable and observable states to derive reduced order models. However, in literature we find disadvantages of balanced truncation approach, because in few of the cases, it fails to guarantee the positive definiteness of associated Gramians, leading to potential instability in reduced models. To address this limitation, several alternate methods have also been proposed in the literature. However, these methods often impose restrictive conditions and may introduce significant approximation errors. Few of these, prove realization-dependent; while others increase computationally complexity, hindering their practical usage.
650			_aPhD Electrical Engineering Thesis _9133107
651			_aPhD EE Thesis _9133108
700			_aSupervised by Dr. Muhammad Imran _9132697
942			_2ddc _cTHE
999			_c616123 _d616123