A practical energy modeling method for industrial robots in manufacturing

Wenjun Xu*, Huan Liu, Jiayi Liu, Zude Zhou, Duc Truong Pham

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Industrial robots (IRs) are widely used in modern manufacturing systems, and energy problem of IRs is paid more attention to meet requirements of environment protection. Therefore, it is necessary to investigate the approaches to optimize the energy consumption of IRs, and the energy consumption model is the basis for enabling such approaches. Usually, energy consumption modeling for IRs is based on dynamic parameters identification. Meanwhile, the physical parameters, e.g. angle, velocity, acceleration, torque, etc. are all the necessary data of parameter identification. However, since the parts of IRs are not easy to be disassembled and the sensor modules can not be installed easily inside IRs, it is difficult to obtain all such physical parameters through sensing method, in particular the torque data. In this context, a practical energy modeling method by measuring total power for IRs is proposed. This method avoids the problem of directly measuring relevant parameters inside IRs, and the parameter identification process is gradually carried out by several excitation experiments. The experimental results show that the proposed energy modeling method can be used to predict the energy consumption of the process used in robot movement in manufacturing processes, and it can also efficiently support the analysis of the energy consumption characteristics of IRs.

Original languageEnglish
Title of host publicationChallenges and Opportunity with Big Data
Subtitle of host publication19th Monterey Workshop 2016, Beijing, China, October 8–11, 2016 Revised Selected Papers
EditorsLin Zhang, Lei Ren, Fabrice Kordon
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319619934
Publication statusPublished - 1 Jan 2017
Event19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016 - Beijing, China
Duration: 8 Oct 201611 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10228 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016


  • Energy consumption
  • Energy modeling
  • Industrial robots
  • Power measurement

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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