scholarly journals Erratum: “A New Evaluation of the Mean Square Integral” (Journal of Dynamic Systems, Measurement, and Control, 1971, 93, pp. 242–246)

1972 ◽  
Vol 94 (1) ◽  
pp. 88-88
Author(s):  
R. J. Beshara
Keyword(s):  
Dynamic Systems ◽  
Mean Square ◽  
The Mean ◽  
And Control ◽  
Measurement And Control

Lateral and Longitudinal Dynamic Behavior and Control of Moving Webs

1993 ◽  
Vol 115 (2B) ◽  
pp. 309-317 ◽  
Author(s):  
G. E. Young ◽  
K. N. Reid
Keyword(s):  
Dynamic Analysis ◽  
Dynamic Behavior ◽  
Dynamic Systems ◽  
Pivotal Role ◽  
Historical Perspectives ◽  
Longitudinal Dynamic ◽  
Primary Direction ◽  
And Control ◽  
Measurement And Control

A web refers to any material in continuous flexible strip form which is either endless or very long compared to its width, and very wide compared to its thickness. This paper discusses the dynamic analysis and control of the lateral and longitudinal motions of a moving web which correspond to fluctuations perpendicular and parallel, respectively, to the primary direction of web transport. Historical perspectives are provided, from the early work of Osborne Reynolds in the late 1800s to current research. An overview of the control of both lateral and longitudinal web motion, which includes the control of tension, is presented. Present limitations in understanding and controlling lateral and longitudinal web behavior are discussed. The Journal of Dynamic Systems, Measurement, and Control has played a pivotal role in the advancement of research in this area.

Random Response Relationships to Transfer Function and State-Space Properties

2007 ◽  
Vol 129 (5) ◽  
pp. 672-677
Author(s):  
Robin C. Redfield
Keyword(s):  
Transfer Function ◽  
State Space ◽  
Dynamic Systems ◽  
System Response ◽  
Mean Square ◽  
System Matrix ◽  
Matrix Transfer Function ◽  
Mean Square Response ◽  
The Mean ◽  
Insight Into

Output variables of dynamic systems subject to random inputs are often quantified by mean-square calculations. Computationally for linear systems, these typically involve integration of the output spectral density over frequency. Numerically, this is a straightforward task and, analytically, methods exist to find mean-square values as functions of transfer function (frequency response) coefficients. These formulations offer analytical relationships between system parameters and mean-square response. This paper develops further analytical relationships in calculating mean-square values as functions of transfer function and state-space properties. Specifically, mean-square response is formulated from (i) system pole-zero locations, (ii) as a spectral decomposition, and (iii) in terms of a system matrix transfer function. Direct, closed-form relationships between response and these properties are afforded. These new analytical representations of the mean-square calculation can provide significant insight into dynamic system response and optimal design/tuning of dynamic systems.

Sign in / Sign up

Export Citation Format

Share Document