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Optimal Design of Agricultural Mobile Robot Suspension System Based on NSGA-III and TOPSIS

Author

Listed:
  • Zhanghao Qu

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

  • Peng Zhang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

  • Yaohua Hu

    (College of Optical, Mechanical, and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China)

  • Huanbo Yang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

  • Taifeng Guo

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

  • Kaili Zhang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

  • Junchang Zhang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

Abstract

The stability of vehicles is influenced by the suspension system. At present, there are many studies on the suspension of traditional passenger vehicles, but few are related to agricultural mobile robots. There are structural differences between the suspension system of agricultural mobile robots and passenger vehicles, which requires structural simplification and modelling concerning suspension of agricultural mobile robots. This study investigates the optimal design for an agricultural mobile robot’s suspension system designed based on a double wishbone suspension structure. The dynamics of the quarter suspension system were modelled based on Lagrange’s equation. In our work, the non-dominated sorting genetic algorithm III (NSGA-III) was selected for conducting multi-objective optimization of the suspension design, combined with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) to choose the optimal combination of parameters in the non-dominated solution set obtained by NSGA-III. We compared the performance of NSGA-III with that of other multi-objective evolutionary algorithms (MOEAs). Compared with the second-scoring solution, the score of the optimal solution obtained by NSGA-III increased by 4.92%, indicating that NSGA-III has a significant advantage in terms of the solution quality and robustness for the optimal design of the suspension system. This was verified by simulation in Adams that our method, which utilizes multibody dynamics, NSGA-III and TOPSIS, is feasible to determine the optimal design of a suspension system for an agricultural mobile robot.

Suggested Citation

  • Zhanghao Qu & Peng Zhang & Yaohua Hu & Huanbo Yang & Taifeng Guo & Kaili Zhang & Junchang Zhang, 2023. "Optimal Design of Agricultural Mobile Robot Suspension System Based on NSGA-III and TOPSIS," Agriculture, MDPI, vol. 13(1), pages 1-20, January.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:1:p:207-:d:1035712
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    References listed on IDEAS

    as
    1. Hugh Everett, 1963. "Generalized Lagrange Multiplier Method for Solving Problems of Optimum Allocation of Resources," Operations Research, INFORMS, vol. 11(3), pages 399-417, June.
    2. Li, Shiying & Xu, Jun & Gao, Haonan & Tao, Tao & Mei, Xuesong, 2020. "Safety probability based multi-objective optimization of energy-harvesting suspension system," Energy, Elsevier, vol. 209(C).
    3. Xiangwang Chen & Yuan Yao & Longjiang Shen & Xiaoxia Zhang, 2022. "Multi-objective optimization of high-speed train suspension parameters for improving hunting stability," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 10(2), pages 159-176, March.
    4. Issa, Mohamed & Samn, Anas, 2022. "Passive vehicle suspension system optimization using Harris Hawk Optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 191(C), pages 328-345.
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    Cited by:

    1. Lei Zhang & Yang Liu & Jianneng Chen & Heng Zhou & Yunsheng Jiang & Junhua Tong & Lianlian Wu, 2023. "Trajectory Synthesis and Optimization Design of an Unmanned Five-Bar Vegetable Factory Packing Machine Based on NSGA-II and Grey Relation Analysis," Agriculture, MDPI, vol. 13(7), pages 1-21, July.
    2. Vadim Bolshev & Vladimir Panchenko & Alexey Sibirev, 2023. "Engineering Innovations in Agriculture," Agriculture, MDPI, vol. 13(7), pages 1-4, June.

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