Adaptive Fuzzy Neural Network Command Filtered Impedance Control of Constrained Robotic Manipulators with Disturbance Observer

Gang Li, Jinpeng Yu, Xinkai Chen

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.

Original languageEnglish
Pages (from-to)5171-5180
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume34
Issue number8
DOIs
Publication statusPublished - 2023 Aug 1

Keywords

  • Command filter
  • disturbance observer
  • full-state constraints
  • fuzzy neural network (NN)
  • impedance control
  • input saturation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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