A Novel Soft Computing Based Algorithm for the Control of Dynamic Uncertain Systems - An Application to DC-DC Converters

Kareem, Abdul; Azeem, Mohammad Fazle

doi:10.5121/ijaia.2011.2202

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2014

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“…(Kareem and Azeem, 2011). Soft computing methods require training of the network through experimental data.…”

Section: Literature Reviewmentioning

confidence: 99%

Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

Koohestani

Yang

2014

Machining Science and Technology

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Significant research effort has been carried out in the detection of chatter, which is one of the main barriers against titanium milling. State-of-the-art techniques are unable to satisfy requirements of industry in terms of in-process chatter detection. The present study reports the use of sensor-signal driven reconstructed phase space attractors combined with image correlation as a solution of chatter prediction during milling of titanium in industry. The method uses Poincaré sections of reconstructed phase space attractor as patterns to identify the onset of chatter in the apparently random behavior of vibrations in the milling process. Image correlation of Poincaré sections indicates the onset of chatter in the milling process.

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“…(Kareem and Azeem, 2011). Soft computing methods require training of the network through experimental data.…”

Section: Literature Reviewmentioning

confidence: 99%

Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

Koohestani

Yang

2014

Machining Science and Technology

View full text Add to dashboard Cite

show abstract

A Novel Soft Computing Based Algorithm for the Control of Dynamic Uncertain Systems - An Application to DC-DC Converters

Abstract: Both Soft computing based controllers and sliding mode controllers have been utilized to regulate the output voltage of dc-dc converters in response to

Cited by 1 publication

References 22 publications

Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

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