Using the bicausality concept to build reduced order observers in linear time invariant systems modelled by bond graph

2003 ◽  
Author(s):  
Cesar Pichardo-Almarza ◽  
Ahmed Rahmani ◽  
Genevieve Dauphin-Tanguy ◽  
Marisol Delgado
Keyword(s):  
Linear Time ◽  
Bond Graph ◽  
Reduced Order ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Time Invariant

scholarly journals Bond graph formulation of an optimal control problem for linear time invariant systems

2008 ◽  
Vol 345 (4) ◽  
pp. 349-373 ◽  
Author(s):  
Wilfrid Marquis-Favre ◽  
Omar Mouhib ◽  
Bogdan Chereji ◽  
Daniel Thomasset ◽  
Jérôme Pousin ◽  
...  
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Linear Time ◽  
Bond Graph ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Time Invariant

A novel model reduction approach for linear time-invariant systems via enhanced PSO-DV algorithm and improved MPPA method

2019 ◽  
Vol 234 (2) ◽  
pp. 240-256
Author(s):  
G Vasu ◽  
M Sivakumar ◽  
M Ramalingaraju
Keyword(s):  
Objective Function ◽  
Linear Time ◽  
Original System ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Linear Time Invariant ◽  
Polynomial Coefficients ◽  
Linear Time Invariant Systems ◽  
Time Invariant

In this article, the combination of stochastic search and conventional approaches are used to develop an optimal frequency-domain model order reduction method for determining the stable and accurate reduced-order model for the stable large-scale linear time-invariant systems. The method uses the enhanced particle swarm optimization with differentially perturbed velocity algorithm to determine the denominator polynomial coefficients of the reduced-order model, whereas the numerator polynomial coefficients of the reduced-order model are determined by using an improved multi-point Padé approximation method. The method generates an optimum reduced-order model by minimizing an objective function [Formula: see text], which is formulated using two functions. The first function, [Formula: see text], evaluates the measure of integral squared error between the step responses of the original system and the reduced-order model. And the second function evaluates the measure of retention of full impulse response energy of the original system in the reduced-order model. Therefore, by minimizing the objective function ‘ E’, the proposed method is guaranteed for preserving passivity, stability and the accuracy of the original higher order system in the reduced-order model. The proposed method is extended to the linear time-invariant multi-input multi-output system. In this case, an optimal reduced-order model is determined by minimizing a single objective function [Formula: see text], which is formulated by linear scalarizing of all the objective function [Formula: see text] components. The method is popular for preserving stability, passivity and accuracy of the original system in the reduced-order model. The validation of the method is shown by applying to a sixth-order single-input single-output hydropower system model as well as to the seventh-order two-area multi-input multi-output power system model. The comparison of the simulation results of integral squared error and impulse response energy values of the reduced-order models demonstrates the dominance of the proposed method than the existing reduction methods available in the literature.

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