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Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network

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@article{IJASEIT1746,
   author = {Mohd Hairi Mohd Zaman and M Marzuki Mustafa and Aini Hussain},
   title = {Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {4},
   year = {2017},
   pages = {1381--1388},
   keywords = {Voltage regulator; output capacitor; equivalent series resistance; failure region boundary; system identification; radial basis function neural network},
   abstract = {

Output capacitor in the voltage regulator (VR) circuit ensures stability especially during fast load transients. However, the capacitor parasitic, namely equivalent series resistance (ESR), may cause unstable VR operation. VR characterization in terms of ESR suggests stable range of capacitor ESR based on the ESR tunnel graph in the VR datasheet. Specifically, the stable ESR range is the critical ESR value, which lies on the failure region boundary of ESR tunnel graph. New or updated ESR tunnel graph through characterization is required for new product development or quality assurance purpose. However, the characterization is typically conducted manually in industry, thereby increases the manufacturing time and cost. Therefore, this work proposed a characterization approach that can reduce the time to determine the ESR tunnel graph based on the hybrid system identification and neural network (SI-NN) approach. This method utilised system identification (SI) to estimate the VR circuit model for certain operating points before predicting the transfer function coefficients for the remaining points using radial basis function neural network (RBFNN). Eventually, the critical ESR of failure region boundary was estimated. This hybrid SI-NN approach able to reduce the number of data that would be acquired manually to 25% compared to manual characterization, while provides critical ESR estimation with error less than 2%.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1746},    doi = {10.18517/ijaseit.7.4.1746} }

EndNote

%A Mohd Zaman, Mohd Hairi
%A Mustafa, M Marzuki
%A Hussain, Aini
%D 2017
%T Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network
%B 2017
%9 Voltage regulator; output capacitor; equivalent series resistance; failure region boundary; system identification; radial basis function neural network
%! Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network
%K Voltage regulator; output capacitor; equivalent series resistance; failure region boundary; system identification; radial basis function neural network
%X 

Output capacitor in the voltage regulator (VR) circuit ensures stability especially during fast load transients. However, the capacitor parasitic, namely equivalent series resistance (ESR), may cause unstable VR operation. VR characterization in terms of ESR suggests stable range of capacitor ESR based on the ESR tunnel graph in the VR datasheet. Specifically, the stable ESR range is the critical ESR value, which lies on the failure region boundary of ESR tunnel graph. New or updated ESR tunnel graph through characterization is required for new product development or quality assurance purpose. However, the characterization is typically conducted manually in industry, thereby increases the manufacturing time and cost. Therefore, this work proposed a characterization approach that can reduce the time to determine the ESR tunnel graph based on the hybrid system identification and neural network (SI-NN) approach. This method utilised system identification (SI) to estimate the VR circuit model for certain operating points before predicting the transfer function coefficients for the remaining points using radial basis function neural network (RBFNN). Eventually, the critical ESR of failure region boundary was estimated. This hybrid SI-NN approach able to reduce the number of data that would be acquired manually to 25% compared to manual characterization, while provides critical ESR estimation with error less than 2%.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1746 %R doi:10.18517/ijaseit.7.4.1746 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 4 %@ 2088-5334

IEEE

Mohd Hairi Mohd Zaman,M Marzuki Mustafa and Aini Hussain,"Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, pp. 1381-1388, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.4.1746.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Mohd Zaman, Mohd Hairi
AU  - Mustafa, M Marzuki
AU  - Hussain, Aini
PY  - 2017
TI  - Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4
Y2  - 2017
SP  - 1381
EP  - 1388
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Voltage regulator; output capacitor; equivalent series resistance; failure region boundary; system identification; radial basis function neural network
N2  - 

Output capacitor in the voltage regulator (VR) circuit ensures stability especially during fast load transients. However, the capacitor parasitic, namely equivalent series resistance (ESR), may cause unstable VR operation. VR characterization in terms of ESR suggests stable range of capacitor ESR based on the ESR tunnel graph in the VR datasheet. Specifically, the stable ESR range is the critical ESR value, which lies on the failure region boundary of ESR tunnel graph. New or updated ESR tunnel graph through characterization is required for new product development or quality assurance purpose. However, the characterization is typically conducted manually in industry, thereby increases the manufacturing time and cost. Therefore, this work proposed a characterization approach that can reduce the time to determine the ESR tunnel graph based on the hybrid system identification and neural network (SI-NN) approach. This method utilised system identification (SI) to estimate the VR circuit model for certain operating points before predicting the transfer function coefficients for the remaining points using radial basis function neural network (RBFNN). Eventually, the critical ESR of failure region boundary was estimated. This hybrid SI-NN approach able to reduce the number of data that would be acquired manually to 25% compared to manual characterization, while provides critical ESR estimation with error less than 2%.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1746 DO - 10.18517/ijaseit.7.4.1746

RefWorks

RT Journal Article
ID 1746
A1 Mohd Zaman, Mohd Hairi
A1 Mustafa, M Marzuki
A1 Hussain, Aini
T1 Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 4
YR 2017
SP 1381
OP 1388
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Voltage regulator; output capacitor; equivalent series resistance; failure region boundary; system identification; radial basis function neural network
AB 

Output capacitor in the voltage regulator (VR) circuit ensures stability especially during fast load transients. However, the capacitor parasitic, namely equivalent series resistance (ESR), may cause unstable VR operation. VR characterization in terms of ESR suggests stable range of capacitor ESR based on the ESR tunnel graph in the VR datasheet. Specifically, the stable ESR range is the critical ESR value, which lies on the failure region boundary of ESR tunnel graph. New or updated ESR tunnel graph through characterization is required for new product development or quality assurance purpose. However, the characterization is typically conducted manually in industry, thereby increases the manufacturing time and cost. Therefore, this work proposed a characterization approach that can reduce the time to determine the ESR tunnel graph based on the hybrid system identification and neural network (SI-NN) approach. This method utilised system identification (SI) to estimate the VR circuit model for certain operating points before predicting the transfer function coefficients for the remaining points using radial basis function neural network (RBFNN). Eventually, the critical ESR of failure region boundary was estimated. This hybrid SI-NN approach able to reduce the number of data that would be acquired manually to 25% compared to manual characterization, while provides critical ESR estimation with error less than 2%.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1746 DO - 10.18517/ijaseit.7.4.1746