Direct design of multivariable control systems through singular value decomposition

1983 ◽  
Author(s):  
L. ANDERSON
Keyword(s):  
Singular Value Decomposition ◽  
Control Systems ◽  
Singular Value ◽  
Multivariable Control ◽  
Multivariable Control Systems ◽  
Direct Design ◽  
Value Decomposition

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.

Structural Analysis of Nonlinear Control Systems Using Singular Value Decomposition: Part II — Control of an Input-Constrained Thermal Process

2003 ◽  
Author(s):  
Kwan-Woong Gwak ◽  
Glenn Y. Masada
Keyword(s):  
Structural Analysis ◽  
Singular Value Decomposition ◽  
Nonlinear Control ◽  
Control Systems ◽  
Singular Value ◽  
Thermal System ◽  
Control Input ◽  
Input Constraints ◽  
Nonlinear Controller ◽  
Value Decomposition

In a Part I (Structural Analysis of Nonlinear Control System using Singular Value Decomposition: Part I—Approach), structural analysis algorithms for nonlinear control systems were developed applying singular value decomposition (SVD) on the linear algebraic equivalence of the nonlinear controller. In this paper, the proposed algorithms are applied to the temperature control of a thermal system which has control input constraints. Control input/output modes, weights of linear mode combination, colinearity, and mode truncation concepts introduced in Part I, are used to analyze and find the cause of input-constraint-violating-control of the nonlinear controller designed for the thermal system and to redesign the nonlinear controller to satisfy the input constraints and to reduce control effort.

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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