Skip to main content
SpringerLink
Log in

Study on Adaptive Fuzzy Control System Based on Gradient Descent Learning Algorithm

  • Conference paper
Fuzzy Information and Engineering

Part of the book series: Advances in Soft Computing ((AINSC,volume 40))

  • 762 Accesses

Abstract

For the general two-dimensional fuzzy controller, the output control value is a rigid function with respect to the error and the change in error, so it is difficult to obtain the desired effects for the plant that is uncertain and time-varying. Aimed at the problem, the paper applies gradient descent learning algorithm to correct the mean and variance of Gaussian membership functions of all fuzzy sets in the input and output universes, in this way, the fuzzy control system is adaptive. Selecting input signals as step, ramp, acceleration and sine signals, respectively, all simulation studies were carried out. The results demonstrate that the control algorithm is feasible, and its effect is better than that of the fuzzy control system without adaptability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Li, S.: Fuzzy Control, Neural Control and Intelligent Cybernetics. Press of Haierbin Institute of Technology, Haierbin (1999)

    Google Scholar 

  2. Wang, L.X.: Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Prentice Hall, Englewood Cliffs (1994)

    Google Scholar 

  3. Jang, J.R.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23, 665–685 (1993)

    Article  Google Scholar 

  4. Lin, C.T.: Neural Fuzzy Control Systems with Structure and Parameter Learning. World Scientific, Singapore (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Information and Automation, Chongqing Institute of Technology, Chongqing, 400050, China

    Jundi Xiong & Huihui Xiao

  2. College of Electronic Information Engineering, Chongqing University of Science and Technology, Chongqing, 400050, China

    Jundi Xiong

  3. College of Automation, Chongqing University, Chongqing, 400044, China

    Renming Deng

Authors
  1. Jundi Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huihui Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Renming Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Bing-Yuan Cao

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xiong, J., Xiao, H., Deng, R. (2007). Study on Adaptive Fuzzy Control System Based on Gradient Descent Learning Algorithm. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_109

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71441-5_109

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71440-8

  • Online ISBN: 978-3-540-71441-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation