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

2 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
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Adaptive systems
  • Artificial neural networks
  • Command filter
  • disturbance observer
  • Disturbance observers
  • full-state constraints
  • fuzzy neural network (NN)
  • Impedance
  • impedance control
  • input saturation.
  • Manipulator dynamics
  • Robots
  • Trajectory

ASJC Scopus subject areas

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

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