scholarly journals Primary shock calibration with fast linear motor drive

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 383
Author(s):  
H. Volkers ◽  
H. C. Schoenekess ◽  
Th. Bruns
Keyword(s):  
Shock Intensity ◽  
Linear Motor ◽  
Pulse Generation ◽  
Substantial Decrease ◽  
Motor Drive ◽  
Peak Acceleration ◽  
Main Challenge ◽  
Shock Generator ◽  
Calibration Device ◽  
Primary Shock

This paper describes the implementation of a new, fast and precise linear motor drive for PTB’s primary shock calibration device. This device is used for monopole shock calibrations of accelerometers using the “hammer-anvil” principle according to ISO 16063-13:2001 and operates in a peak acceleration range from 50 m/s² to 5000 m/s². <br />The main challenge of implementing this kind of shock generator is accelerating a hammer to velocities up to 5 m/s within distances of less than 70 mm. <br />In this paper, a few helpful improvements are described which lead to an enhanced repeatability of pulse generation over the full shock intensity range as well as a substantial decrease of harmonic disturbing signals.

Indirect Neural Network Adaptive Robust Control of Linear Motor Drive System

2002 ◽  
Author(s):  
J. Q. Gong ◽  
Bin Yao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Tracking Error ◽  
Linear Motor ◽  
Adaptive Robust Control ◽  
Operating Conditions ◽  
Tracking Performance ◽  
Motor Drive ◽  
Output Tracking ◽  
On Line

In this paper, an indirect neural network adaptive robust control (INNARC) scheme is developed for the precision motion control of linear motor drive systems. The proposed INNARC achieves not only good output tracking performance but also excellent identifications of unknown nonlinear forces in system for secondary purposes such as prognostics and machine health monitoring. Such dual objectives are accomplished through the complete separation of unknown nonlinearity estimation via neural networks and the design of baseline adaptive robust control (ARC) law for output tracking performance. Specifically, recurrent neural network (NN) structure with NN weights tuned on-line is employed to approximate various unknown nonlinear forces of the system having unknown forms to adapt to various operating conditions. The design is actual system dynamics based, which makes the resulting on-line weight tuning law much more robust and accurate than those in the tracking error dynamics based direct NNARC designs in implementation. With a controlled learning process achieved through projection type weights adaptation laws, certain robust control terms are constructed to attenuate the effect of possibly large transient modelling error for a theoretically guaranteed robust output tracking performance in general. Experimental results are obtained to verify the effectiveness of the proposed INNARC strategy. For example, for a typical point-to-point movement, with a measurement resolution level of ±1μm, the output tracking error during the entire execution period is within ±5μm and mainly stays within ±2μm showing excellent output tracking performance. At the same time, the outputs of NNs approximate the unknown forces very well allowing the estimates to be used for secondary purposes such as prognostics.

High Efficient Pulse Tube Refrigerator with Linear-Motor Drive Compressor

1994 ◽  
pp. 1441-1448 ◽  
Author(s):  
Yasumi Ohtani ◽  
Hideo Hatakeyama ◽  
Toru Kuriyama ◽  
Hideki Nakagome ◽  
Yoichi Matsubara ◽  
...  
Keyword(s):  
Linear Motor ◽  
Motor Drive ◽  
Pulse Tube ◽  
Pulse Tube Refrigerator ◽  
High Efficient
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