Adaptive neural network based tracking control for electrically driven flexible-joint robots without velocity measurements

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

Research output: Contribution to journalArticle

15 Citations (Scopus)


This paper addresses the motion tracking control for a class of flexible-joint robotic manipulators actuated by brushed direct current motors. This class of electrically driven flexible-joint robots is perturbed by plant uncertainties and external disturbances. Adaptive neural network systems are employed to approximate the behaviors of uncertain mechanical and electrical dynamics. A reduced-order observer is constructed to estimate the velocity signals. Only the measurements of link position and armature current are required for feedback. Consequently, an adaptive neural network-based dynamic feedback tracking controller without velocity measurements is developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking errors can be made as small as possible. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control algorithms.

Original languageEnglish
Pages (from-to)1022-1032
Number of pages11
JournalComputers and Mathematics with Applications
Issue number5
Publication statusPublished - 2012 Sep 1


All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics

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