On-line pulse control for structural and mechanical systems

1981 ◽  
Author(s):  
F. UDWADIA ◽  
J. GARBA
Keyword(s):  
Mechanical Systems ◽  
Pulse Control ◽  
On Line

An Analysis of Pulse Control for Simple Mechanical Systems

1985 ◽  
Vol 107 (2) ◽  
pp. 123-131 ◽  
Author(s):  
Z. Prucz ◽  
T. T. Soong ◽  
A. Reinhorn
Keyword(s):  
Control Algorithm ◽  
Control Method ◽  
Excitation Frequency ◽  
Mechanical Systems ◽  
System Response ◽  
Control Parameters ◽  
Pulse Control ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
On Line

An efficient pulse control method for insuring safety of simple mechanical systems is developed and its sensitivity to the excitation frequency content and to various control parameters is studied. The control algorithm, consisting of applying pulse forces in a feedback fashion, is designed to insure that maximum system response is limited to safe values at all times. It is shown that the proposed algorithm is simple to implement and is efficient in controlling peak response in terms of on-line computation and pulse energy required. The technique is illustrated and analyzed for a single-degree-of-freedom linear system.

Pulse Control of Structural and Mechanical Systems

1981 ◽  
Vol 107 (6) ◽  
pp. 1011-1028
Author(s):  
Firdaus E. Udwadia ◽  
Sayeed Tabaie
Keyword(s):  
Mechanical Systems ◽  
Pulse Control

Neural Network Based Tracking Control of Mechanical Systems

1999 ◽  
Vol 121 (1) ◽  
pp. 148-154
Author(s):  
T. Efrati ◽  
H. Flashner
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Optimization Problem ◽  
Simple Structure ◽  
Tracking Error ◽  
Mechanical Systems ◽  
Closed Loop System ◽  
Control Effort ◽  
Learning Procedure ◽  
On Line

A method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural network. It is shown that the tracking error of the closed-loop system goes to zero while the control effort is minimized. Tuning of the neural network’s weights is formulated in terms of a constrained optimization problem. The resulting algorithm has a simple structure and requires a very modest computation effort. In addition, the neural network’s learning procedure is implemented on-line.

scholarly journals On-Line Distinction Methods of Human Falling Motions by Machine Learning(Mechanical Systems)

2010 ◽  
Vol 76 (772) ◽  
pp. 3704-3713
Author(s):  
Shunichi AOYAGI ◽  
Yuichi CHIDA ◽  
Tetsuji YATSUNAMI ◽  
Teruyuki NISHIMURA ◽  
Satoshi ASAWA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Systems ◽  
On Line

The on-line use of histogram data for high-speed correction and alignment of electron microscope images

1984 ◽  
Vol 42 ◽  
pp. 556-557
Author(s):  
William Krakow
Keyword(s):  
Power Spectrum ◽  
High Speed ◽  
Direct Memory Access ◽  
Digital Television ◽  
Contrast Transfer Function ◽  
The Past ◽  
High Resolution Tem ◽  
On Line ◽  
Correction Of Astigmatism ◽  
The Fourier Transform

In the past few years on-line digital television frame store devices coupled to computers have been employed to attempt to measure the microscope parameters of defocus and astigmatism. The ultimate goal of such tasks is to fully adjust the operating parameters of the microscope and obtain an optimum image for viewing in terms of its information content. The initial approach to this problem, for high resolution TEM imaging, was to obtain the power spectrum from the Fourier transform of an image, find the contrast transfer function oscillation maxima, and subsequently correct the image. This technique requires a fast computer, a direct memory access device and even an array processor to accomplish these tasks on limited size arrays in a few seconds per image. It is not clear that the power spectrum could be used for more than defocus correction since the correction of astigmatism is a formidable problem of pattern recognition.

Image registration with a computer driven S.T.E.M.

1978 ◽  
Vol 36 (1) ◽  
pp. 98-99
Author(s):  
A.M.H. Schepman ◽  
J.A.P. van der Voort ◽  
J.E. Mellema
Keyword(s):  
Spot Size ◽  
Scanning Transmission Electron Microscope ◽  
Small Computer ◽  
Structural Studies ◽  
Area Of Interest ◽  
Transmission Electron ◽  
Scanning Transmission ◽  
Specimen Area ◽  
On Line ◽  
Minimum Distances

A Scanning Transmission Electron Microscope (STEM) was coupled to a small computer. The system (see Fig. 1) has been built using a Philips EM400, equipped with a scanning attachment and a DEC PDP11/34 computer with 34K memory. The gun (Fig. 2) consists of a continuously renewed tip of radius 0.2 to 0.4 μm of a tungsten wire heated just below its melting point by a focussed laser beam (1). On-line operation procedures were developped aiming at the reduction of the amount of radiation of the specimen area of interest, while selecting the various imaging parameters and upon registration of the information content. Whereas the theoretical limiting spot size is 0.75 nm (2), routine resolution checks showed minimum distances in the order 1.2 to 1.5 nm between corresponding intensity maxima in successive scans. This value is sufficient for structural studies of regular biological material to test the performance of STEM over high resolution CTEM.

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