• Apr 24, 2017 News! Vol.6, No.4 has been indexed by EI (Inspec).   [Click]
  • Apr 24, 2017 News! Vol.6, No.3 has been indexed by EI (Inspec).   [Click]
  • Jun 26, 2017 News!Vol 7, No 4 has been published with online version 12 original aritcles from 6 countries are published in this issue   [Click]
General Information
    • ISSN: 2010-3697
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Engineering & Technology Digital Library, ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET).
    • E-mail ijmo@iacsitp.com
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2016 Vol.6(6): 330-336 ISSN: 2010-3697
DOI: 10.7763/IJMO.2016.V6.547

Parameter Identification of a Shell Transfer Arm Using PSO and Similarity Degree Analysis

Qiang-Qiang Zhao and Bao-Lin Hou
Abstract—A shell transfer arm is a complex mechanical electro-hydraulic system with multiple parameters. An accurate dynamic model is the precondition of improved design. However, during the modeling process, several parameters that have a significant effect on the performance of the system can be achieved only by identification methods because they are hard to measure. According to the structure and control theory of the shell transfer arm, the analytical model and control model are built. To evaluate the level of similarity of the built model and real system, the numerical similarity and shape similarity of the angular velocity of the arm are calculated, and the combination similarity of numerical similarity and shape similarity is set as the fitness function of Particle Swarm Optimization (PSO). Four unknown parameters are identified by PSO. The identification results with respect to the simulation data show that the identification accuracy of PSO can acquires the requirement. And the identification results with respect to the test data show that the built model is accurate, and parameter identification of the shell transfer arm based on PSO and similarity degree analysis of time-vary data is feasible and effective.

Index Terms—Shell transfer arm, dynamic modeling, parameter identification, particle swarm optimization, similarity degree analysis.

Q. Q. Zhao is with School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 China (e-mail: zqqlzl@ 139.com).
B. L. Hou is with School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 China (e-mail: houbl@njust.edu.cn).

[PDF]

Cite: Qiang-Qiang Zhao and Bao-Lin Hou, "Parameter Identification of a Shell Transfer Arm Using PSO and Similarity Degree Analysis," International Journal of Modeling and Optimization vol. 6, no. 6, pp. 330-336, 2016.

Copyright © 2008-2015.International Journal of Modeling and Optimization. All rights reserved.
E-mail: ijmo@iacsitp.com