A nonlinear model reduction formulation for power system slow coherency and aggregation

2005 ◽  
pp. 282-298 ◽  
Author(s):  
Joe H. Chow
Keyword(s):  
Power System ◽  
Model Reduction ◽  
Nonlinear Model ◽  
Nonlinear Model Reduction

Nonlinear Model Reduction by Moment Matching

2017 ◽  
Author(s):  
Giordano Scarciotti ◽  
Alessandro Astolfi
Keyword(s):  
Model Reduction ◽  
Nonlinear Model ◽  
Moment Matching ◽  
Nonlinear Model Reduction

scholarly journals Empirical differential Gramians for nonlinear model reduction

Automatica ◽  
2021 ◽  
Vol 127 ◽  
pp. 109534
Author(s):  
Yu Kawano ◽  
Jacquelien M.A. Scherpen
Keyword(s):  
Model Reduction ◽  
Nonlinear Model ◽  
Nonlinear Model Reduction

scholarly journals High-order differentiable autoencoder for nonlinear model reduction

2021 ◽  
Vol 40 (4) ◽  
pp. 1-15
Author(s):  
Siyuan Shen ◽  
Yin Yang ◽  
Tianjia Shao ◽  
He Wang ◽  
Chenfanfu Jiang ◽  
...  
Keyword(s):  
Model Reduction ◽  
Nonlinear Model ◽  
High Order ◽  
Nonlinear Model Reduction

scholarly journals Nonlinear model reduction of solute transport models

2019 ◽  
Vol 130 ◽  
pp. 157-171
Author(s):  
Zachary P. Stanko ◽  
William W.-G. Yeh
Keyword(s):  
Solute Transport ◽  
Model Reduction ◽  
Nonlinear Model ◽  
Transport Models ◽  
Nonlinear Model Reduction

Nonlinear Model Reduction for Automotive System Descriptions

2001 ◽  
Author(s):  
Zeyu Liu ◽  
John Wagner
Keyword(s):  
Dynamic System ◽  
Model Reduction ◽  
Nonlinear Model ◽  
Order System ◽  
Control Algorithms ◽  
Model Based Control ◽  
Reduction Strategy ◽  
Nonlinear Model Reduction ◽  
Model Based ◽  
Model Free

Abstract The mathematical modeling of dynamic systems is an important task in the design, analysis, and implementation of advanced automotive control systems. Although most vehicle control algorithms tend to use model-free calibration architectures, a need exists to migrate to model-based control algorithms which offer greater operating performance. However, in many instances, the analytical descriptions are too complex for real-time powertrain and chassis model-based control algorithms. Therefore, model reduction strategies may be applied to transform the original model into a simplified lower-order form while preserving the dynamic characteristics of the original high-order system. In this paper, an empirical gramian balanced nonlinear model reduction strategy is examined for the simplification process of dynamic system descriptions. The empirical gramians may be computed using either experimental or simulation data. These gramians are then balanced and unimportant system dynamics truncated. For comparison purposes, a Taylor Series linearization will also be introduced to linearize the original nonlinear system about an equilibrium operating point and then a balanced realization linear reduction strategy will be applied. To demonstrate the functionality of each model reduction strategy, two nonlinear dynamic system models are investigated and respective transient performances compared.

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