Robust neural network-based tracking control for electrically driven constrained robots with constraint uncertainties

Tzuu Hseng S. Li, Hui Min Yen, Yeong Chan Chang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper addresses the problem of designing robust tracking controls for constrained robot systems actuated by brushed direct current motors. Both mechanical dynamics and electrical dynamics in the electrically driven constrained mechanical system are unknown and neural network approximation systems are constructed to learn the behaviors of these two uncertain terms. Moreover, the constraint surface can be allowed to be perturbed by time-varying bounded uncertainties. By using the backstepping technique, an adaptive neural network-based dynamic feedback tracking controller is developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error can be made as small as possible.

Original languageEnglish
Pages (from-to)97-101
Number of pages5
JournalInternational Journal of Nonlinear Sciences and Numerical Simulation
Volume11
Publication statusPublished - 2010 Dec 1

Fingerprint

Tracking Control
robots
Robot
Robots
Neural Networks
Neural networks
Uncertainty
Backstepping
Trajectory Tracking
Constrained Systems
Robust Control
feedback control
Closed loop systems
Mechanical Systems
Closed-loop System
Time-varying
controllers
direct current
Trajectories
trajectories

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Computational Mechanics
  • Modelling and Simulation
  • Engineering (miscellaneous)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Applied Mathematics

Cite this

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AB - This paper addresses the problem of designing robust tracking controls for constrained robot systems actuated by brushed direct current motors. Both mechanical dynamics and electrical dynamics in the electrically driven constrained mechanical system are unknown and neural network approximation systems are constructed to learn the behaviors of these two uncertain terms. Moreover, the constraint surface can be allowed to be perturbed by time-varying bounded uncertainties. By using the backstepping technique, an adaptive neural network-based dynamic feedback tracking controller is developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error can be made as small as possible.

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