Transmission line model with frequency dependency and propagation effects: A model order reduction and state-space approach

2008 ◽  
Author(s):  
Norberto Garcia ◽  
Enrique Acha
Keyword(s):  
Transmission Line ◽  
State Space ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Transmission Line Model ◽  
Model Order ◽  
State Space Approach ◽  
Frequency Dependency ◽  
Propagation Effects ◽  
Space Approach

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

2011 ◽  
Vol 21 (3) ◽  
pp. 509-519 ◽  
Author(s):  
Jérôme Guillet ◽  
Benjamin Mourllion ◽  
Abderazik Birouche ◽  
Michel Basset
Keyword(s):  
State Space ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Second Order ◽  
Input State ◽  
Model Order ◽  
New Approach ◽  
Order Form ◽  
Form Model ◽  
Single Input

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

Subspace Trajectory Piecewise-Linear Model Order Reduction for Nonlinear Circuits

2013 ◽  
Vol 14 (3) ◽  
pp. 639-663 ◽  
Author(s):  
Xiaoda Pan ◽  
Hengliang Zhu ◽  
Fan Yang ◽  
Xuan Zeng
Keyword(s):  
State Space ◽  
Model Order Reduction ◽  
Large Scale ◽  
Piecewise Linear ◽  
Order Reduction ◽  
Nonlinear Circuits ◽  
Model Order ◽  
Reduced Order ◽  
Dimensional State Space ◽  
Model Size

AbstractDespite the efficiency of trajectory piecewise-linear (TPWL) model order reduction (MOR) for nonlinear circuits, it needs large amount of expansion points for large-scale nonlinear circuits. This will inevitably increase the model size as well as the simulation time of the resulting reduced macromodels. In this paper, subspace TPWL-MOR approach is developed for the model order reduction of nonlinear circuits. By breaking the high-dimensional state space into several subspaces with much lower dimensions, the subspace TPWL-MOR has very promising advantages of reducing the number of expansion points as well as increasing the effective region of the reduced-order model in the state space. As a result, the model size and the accuracy of the TWPL model can be greatly improved. The numerical results have shown dramatic reduction in the model size as well as the improvement in accuracy by using the subspace TPWL-MOR compared with the conventional TPWL-MOR approach.

An Automated Model-Order Reduction Method for Automatic Transmissions

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Vanja Ranogajec ◽  
Joško Deur
Keyword(s):  
State Space ◽  
Model Order Reduction ◽  
Reduction Method ◽  
State Space Model ◽  
Order Reduction ◽  
Torque Converter ◽  
State Variables ◽  
Model Order ◽  
Automatic Transmissions ◽  
Order Reduction Method

New generation of torque converter automatic transmissions (AT) includes a large number of gears for improved fuel economy and vehicle performance, which leads to exponentially increasing number of shift types and shift events. In order to facilitate various numerical/simulation analyses of AT dynamics, shift control optimization, and control strategy design, a full-order AT model is usually reduced by eliminating state variables related to locked clutches in particular gears or shifts. The paper proposes an automated model-order reduction method for an arbitrary, user-specified clutch state, and demonstrates its application on an example of ten-speed AT. The method is based on determining the locked-clutch torque variables and their substitution into the full-order state-space model input vector, as well as finding a linear relation between the reduced-order and full-order model state-space variables.

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