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Developing a New Robust Swarm-Based Algorithm for Robot Analysis

Author

Listed:
  • Abubakar Umar

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Zhanqun Shi

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Alhadi Khlil

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Zulfiqar I. B. Farouk

    (School of Mechanical engineering, Tianjin University, Tianjin 300350, China)

Abstract

Metaheuristics are incapable of analyzing robot problems without being enhanced, modified, or hybridized. Enhanced metaheuristics reported in other works of literature are problem-specific and often not suitable for analyzing other robot configurations. The parameters of standard particle swarm optimization (SPSO) were shown to be incapable of resolving robot optimization problems. A novel algorithm for robot kinematic analysis with enhanced parameters is hereby presented. The algorithm is capable of analyzing all the known robot configurations. This was achieved by studying the convergence behavior of PSO under various robot configurations, with a view of determining new PSO parameters for robot analysis and a suitable adaptive technique for parameter identification. Most of the parameters tested stagnated in the vicinity of strong local minimizers. A few parameters escaped stagnation but were incapable of finding the global minimum solution, this is undesirable because accuracy is an important criterion for robot analysis and control. The algorithm was trained to identify stagnating solutions. The algorithm proposed herein was found to compete favorably with other algorithms reported in the literature. There is a great potential of further expanding the findings herein for dynamic parameter identification.

Suggested Citation

  • Abubakar Umar & Zhanqun Shi & Alhadi Khlil & Zulfiqar I. B. Farouk, 2020. "Developing a New Robust Swarm-Based Algorithm for Robot Analysis," Mathematics, MDPI, vol. 8(2), pages 1-30, January.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:158-:d:312131
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    References listed on IDEAS

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    1. Li Wang & Jie Zhao & Dichen Liu & Yi Lin & Yu Zhao & Zhangsui Lin & Ting Zhao & Yong Lei, 2017. "Parameter Identification with the Random Perturbation Particle Swarm Optimization Method and Sensitivity Analysis of an Advanced Pressurized Water Reactor Nuclear Power Plant Model for Power Systems," Energies, MDPI, vol. 10(2), pages 1-22, February.
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    Cited by:

    1. Rizqi, Zakka Ugih & Chou, Shuo-Yan & Yu, Tiffany Hui-Kuang, 2023. "Green energy mix modeling under supply uncertainty: Hybrid system dynamics and adaptive PSO approach," Applied Energy, Elsevier, vol. 349(C).

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