A Practical Energy Modeling Method for Industrial Robots in Manufacturing

  • Wenjun XuEmail author
  • Huan Liu
  • Jiayi Liu
  • Zude Zhou
  • Duc Truong Pham
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10228)


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.


Industrial robots Energy modeling Energy consumption Power measurement 



This research is supported by National Natural Science Foundation of China (Grant No. 51305319), the International Science & and Technology Cooperation Program of China (Grant No. 2015DFA70340), and Engineering and Physical Sciences Research Council (EPSRC), UK (Grant No. EP/N018524/1).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wenjun Xu
    • 1
    • 2
    Email author
  • Huan Liu
    • 1
    • 2
  • Jiayi Liu
    • 1
    • 2
  • Zude Zhou
    • 1
    • 2
  • Duc Truong Pham
    • 3
  1. 1.School of Information EngineeringWuhan University of TechnologyWuhanChina
  2. 2.Key Laboratory of Fiber Optic Sensing Technology and Information ProcessingMinistry of EducationWuhanChina
  3. 3.Department of Mechanical Engineering, School of EngineeringUniversity of BirminghamBirminghamUK

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