Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Improving PID control based on Neural Network
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2018 (English)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

PID is a prevalent tool of automatic control in both industry and home environment, and PID parameters are often forced to modify because of systematic service on the machines or systems, which is time-costing. The project aims to investigate the possibility of applying neural network and reducing PID configuration in controlling industry process, by means of establishing control models and comparing control performance between conventional PID method and improved PID control based on neural network where two built neural networks are considered as cores to adjust weights which result in the suggested PID parameters. Adaptive learning rate is also applied which is adjusted by the algorithm based on the error changes. Algorithm program is written in Siemens TIA Portal and simulated in Factory I/O. In general, the simulations after analysis have shown that the proposed model has a better performance than conventional PID in terms of steady state, deviations and consistency of control value except tuning time. In the future the author is dedicated to continue improving the mentioned model through quickening learning process, applying better activation function and modifying variable structure and so on.

Place, publisher, year, edition, pages
2018. , p. 33
Keywords [en]
PID, neural network, PLC, TIA Portal
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:miun:diva-33916Local ID: ET-V18-G3-012OAI: oai:DiVA.org:miun-33916DiVA, id: diva2:1223832
Subject / course
Electrical Engineering ET2
Educational program
Automationsingenjör TAUMG 180 GR
Supervisors
Examiners
Available from: 2018-06-26 Created: 2018-06-26 Last updated: 2018-06-26Bibliographically approved

Open Access in DiVA

fulltext(3284 kB)31 downloads
File information
File name FULLTEXT01.pdfFile size 3284 kBChecksum SHA-512
2d6e7d343c6fbb4e4ccddd853e3e62f569da76fafe9573d95645ffd0941c4078f2494ba966647f8276d93a4f823dca4d95823058ac12aeb23d19b9bebb722ecf
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Li, Jun
By organisation
Department of Electronics Design
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 31 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 67 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf