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

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

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)97-101
頁數5
期刊International Journal of Nonlinear Sciences and Numerical Simulation
11
DOIs
出版狀態Published - 2010 十二月 1

All Science Journal Classification (ASJC) codes

  • 統計與非線性物理學
  • 計算力學
  • 建模與模擬
  • 工程(雜項)
  • 材料力學
  • 物理與天文學 (全部)
  • 應用數學

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