Computing fluid‐coupled resonance frequencies, mode‐shapes, and damping loss factors using the singular value decomposition

2003 ◽  
Vol 114 (4) ◽  
pp. 2332-2332
Author(s):  
John B. Fahnline
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Mode Shapes ◽  
Resonance Frequencies ◽  
Value Decomposition ◽  
Loss Factors

Structural Analysis of Nonlinear Control Systems Using Singular Value Decomposition: Part I — Approach

2003 ◽  
Author(s):  
Kwan-Woong Gwak ◽  
Glenn Y. Masada
Keyword(s):  
Structural Analysis ◽  
Singular Value Decomposition ◽  
Nonlinear Control ◽  
Control Systems ◽  
Singular Value ◽  
Mode Shapes ◽  
Nonlinear Control Systems ◽  
Control Input ◽  
Input Output ◽  
Value Decomposition

Structural information of a system/controller allows a designer to diagnose performance characteristics in advance and to make better choices of solution methods. Singular value decomposition (SVD) is a powerful structural analysis tool that has been applied to linear systems and controller designs, but it has not been used for nonlinear systems. In this paper, SVD is use to structurally analyze and to optimally design nonlinear control systems using the linear algebraic equivalence of the nonlinear controller. Specifically, SVD is used to identify control input/output mode shapes, and the control input/output distribution patterns are analyzed with the mode shapes. Optimizing control effort and performance is achieved by truncating some mode shapes in the linear mode shape combinations.

Computer-Aided Structure Modification of Electromechancal Systems Using Singular Value Decomposition

1992 ◽  
Author(s):  
Subhendu Rai ◽  
Haruhiko Asada
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Mode Shapes ◽  
Structure Modification ◽  
Modification System ◽  
Positioning Systems ◽  
Design Changes ◽  
Computer Aided ◽  
Value Decomposition ◽  
System Characteristics

Abstract A computer-aided design method for modifying the mechanical structure of electro-mechanical systems, eg. robots and positioning systems, is presented in this paper. The pole-zero locations and other system characteristics are represented in relation to the structural geometry by using a finite element method. The inverse problem of determining a geometry which will yield better dynamic characteristics is formulated and solved. Based on sensitivity analysis and the singular value decomposition of the sensitivity matrix, useful information is provided to the designer for making effective design changes and understanding trade-offs among multiple system characteristics to be improved. This decomposition also provides us with a new type of geometric design mode-shapes that characterizes all possible design changes affecting system characteristics. The design methodology is implemented to develop a computer-aided structure modification system. A design example of a flexible robot arm demonstrates the usefulness of the system.

Structural Analysis and Optimization of Nonlinear Control Systems Using Singular Value Decomposition

2004 ◽  
Vol 127 (1) ◽  
pp. 105-113 ◽  
Author(s):  
Kwan-Woong Gwak ◽  
Glenn Y. Masada
Keyword(s):  
Structural Analysis ◽  
Singular Value Decomposition ◽  
Nonlinear Control ◽  
Control Systems ◽  
Singular Value ◽  
Mode Shapes ◽  
Nonlinear Control Systems ◽  
Control Input ◽  
Input Output ◽  
Value Decomposition

Structural information of a system/controller allows a designer to diagnose performance characteristics in advance and to make better choices of solution methods. Singular value decomposition (SVD) is a powerful structural analysis tool for linear systems, but it has not been applied to nonlinear systems. In this paper, SVD is used to structurally analyze and to optimally design nonlinear control systems using the linear algebraic equivalence of the nonlinear controller. Specifically, SVD is used to identify control input/output mode shapes, and the control input/output distribution patterns are analyzed using the mode shapes. Optimizing control effort and performance is achieved by truncating some mode shapes in the linear mode shape combinations. The proposed method is applied to the temperature control of a thermal system, and design guidelines are provided to overcome input-constraint-violating solutions.

Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter

2017 ◽  
Author(s):  
Ammar Ismael Kadhim ◽  
Yu-N Cheah ◽  
Inaam Abbas Hieder ◽  
Rawaa Ahmed Ali
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition
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