A novel robust PID controllers design by fuzzy neural network

Ching Hung Lee, Yi Hsiung Lee, Ching Cheng Teng

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

In the paper, we propose a robust PID tuning method using fuzzy neural network (FNN) based on robust gain and phase margin (GM/PM) specifications. The designed PID controller is available for the interval plant family. We can use the trained FNN system to determine the parameters of PID controllers that are based on the robust GM/PM. To determine the robust GM/PM, the Kharitonov 32 extreme systems are used. Therefore, the FNN system is able to automatically tune the PID controller parameters with different GM/PM specifications, so that neither numerical methods nor graphical methods have to be used. This makes it easy to tune the controller parameters to have the specified robustness and performance. Simulation results are shown to illustrate the effectiveness of the robust PID controller scheme.

Original languageEnglish
Pages (from-to)1561-1566
Number of pages6
JournalProceedings of the American Control Conference
Volume2
DOIs
StatePublished - 2002

Keywords

  • Fuzzy neural network
  • Gain margin
  • Kharitonov theorem
  • Phase margin
  • PID control
  • Robust control

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