Extracting second-order structures from single-input state-space models: Application to model order reduction

Abstract

This paper focuses on the model order reduction problem of second-order form models. The aim is to provide a reduction procedure which guarantees the preservation of the physical structural conditions of second-order form models. To solve this problem, a new approach has been developed to transform a second-order form model from a state-space realization which ensures the preservation of the structural conditions. This new approach is designed for controllable single-input state-space realizations with real matrices and has been applied to reduce a single-input second-order form model by balanced truncation and modal truncation.

Stability Constraints of Markov State Kinetic Models Based on Routh- Hurwitz Criterion

“…Notice that computational neuroscience often involves models of ever increasing computational complexity [35], our team has already invested significant efforts in reduction of model complexity. Another orientation work may be considered, in parallel to the studied stability constraints here, in investigating a reduction of model order [36,37], while guaranteeing its stability and maintaining desired nonlinear dynamic response.…”

The INOVE ANR 2010 Blan 0308 project: Integrated approach for observation and control of vehicle dynamics

“…Different approaches introducing reachability and observability Grammians of the considered systems, have been developed leading to a balanced truncation model reduction procedure. Such approaches have been tested on different systems as switched systems (considering switched models for observation and control in critical driving situations) [1], [12] and robust controllers to keep performance and regulation considering uncertainties [35].…”