Adaptive prescribed performance motion control of servo mechanisms with friction compensation

Jing Na, Qiang Chen, Xuemei Ren, Yu Guo

Research output: Contribution to journalArticle (Academic Journal)peer-review

294 Citations (Scopus)

Abstract

This paper proposes an adaptive control for a class of nonlinear mechanisms with guaranteed transient and steady-state performance. A performance function characterizing the convergence rate, maximum overshoot, and steady-state error is used for the output error transformation, such that stabilizing the transformed system is sufficient to achieve the tracking control of the original system with a priori prescribed performance. A continuously differentiable friction model is adopted to account for the friction nonlinearities, for which primary model parameters are online updated. A novel high-order neural network with only a scalar weight is developed to approximate unknown nonlinearities and to dramatically diminish the computational costs. Comparative experiments on a turntable servo system are included to verify the reliability and effectiveness.

Original languageEnglish
Article number6413221
Pages (from-to)486-494
Number of pages9
JournalIEEE Transactions on Industrial Electronics
Volume61
Issue number1
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Adaptive control
  • Friction compensation
  • Motion control
  • Neural networks (NNs)
  • Servo mechanisms

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