Input-Output Fault Diagnosis in Robot Manipulator Using Fuzzy LMI-Tuned PI Feedback Linearization Observer Based on Nonlinear Intelligent ARX Model

Farzin Piltan, Manjurul Islam, Jong-Myon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper proposes a model-based fault detection and diagnosis (FDD) technique for six degrees of freedom PUMA robot manipulator in presence of noise in actuator and sensor faults. The inverse modeling based on an adaptive method, which combines the fuzzy C-means clustering with the modified autoregressive eXternal (ARX) model, is presented for the system identification. The proposed adaptive nonlinear ARX fuzzy C-means (NARXNF) clustering technique obtains an improved convergence and error reduction than that of the traditional fuzzy C-means clustering algorithm. In addition, proportional integral (PI) feedback linearization observation is used for diagnosing the fault, where the convergence, robustness, and stability are validated by fuzzy linear matrix inequality (FLMI). Experimental results, in presence of 40% noise, show that the rate of root mean square (RMS) error for end-effector position is less than 0.00624. The proposed method also improves the rate of sensors and actuators FDD without additional hardware.
Original languageEnglish
Title of host publicationAdvances in Computer Communication and Computational Sciences
Pages305-315
Number of pages11
Volume759
ISBN (Electronic)978-981-13-0341-8
DOIs
Publication statusPublished (in print/issue) - 23 Aug 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Keywords

  • Inverse modeling
  • Fault diagnosis
  • Fuzzy C-means clustering
  • Autoregressive external model
  • PI feedback linearization observation

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