Neural Network-Based Joint Velocity Estimation Method for Improving Robot Control Performance

Dongwhan Kim, Soonwook Hwang, Myotaeg Lim, Yonghwan Oh, Yisoo Lee

Research output: Contribution to journalArticlepeer-review


Joint velocity estimation is one of the essential properties that implement for accurate robot motion control. Although conventional approaches such as numerical differentiation of position measurements and model-based observers exhibit feasible performance for velocity estimation, instability can be occurred because of phase lag or model inaccuracy. This study proposes a model-free approach that can estimate the velocity with less phase lag by batch training of a neural network with pre-collected encoder measurements. By learning a weighted moving average, the proposed method successfully estimates the velocity with less latency imposed by the noise attenuation compared to the conventional methods. Practical experiments with two robot platforms with high degrees of freedom are conducted to validate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)130517-130526
Number of pages10
JournalIEEE Access
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors.


  • Robotics
  • machine learning
  • neural network
  • robot control
  • state estimation

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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