Critical equivalent series resistance estimation for voltage regulator stability using hybrid system identification and neural network

Research output: Contribution to journalArticle

2 Citations (Scopus)

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%.

Original languageEnglish
Pages (from-to)1381-1388
Number of pages8
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume7
Issue number4
DOIs
Publication statusPublished - 2017

Fingerprint

Voltage regulators
Hybrid systems
neural networks
Identification (control systems)
Neural networks
Tunnels
Capacitors
Industry
product development
new products
Costs and Cost Analysis
product quality
quality control
manufacturing
Networks (circuits)
industry
Quality assurance
Product development
Transfer functions
Costs

Keywords

  • Equivalent series resistance
  • Failure region boundary
  • Output capacitor
  • Radial basis function neural network
  • System identification
  • Voltage regulator

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Computer Science(all)
  • Engineering(all)

Cite this

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title = "Critical equivalent series resistance estimation for voltage regulator stability using hybrid system identification and 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{\%}.",
keywords = "Equivalent series resistance, Failure region boundary, Output capacitor, Radial basis function neural network, System identification, Voltage regulator",
author = "{Mohd Zaman}, {Mohd Hairi} and Mustafa, {Mohd. Marzuki} and Aini Hussain",
year = "2017",
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AU - Mohd Zaman, Mohd Hairi

AU - Mustafa, Mohd. Marzuki

AU - Hussain, Aini

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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%.

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%.

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KW - Failure region boundary

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KW - System identification

KW - Voltage regulator

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