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Regression-Based Correction and I-PSO-Based Optimization of HMCVT’s Speed Regulating Characteristics for Agricultural Machinery

Author

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  • Zhun Cheng

    (Department of Vehicle Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Zhixiong Lu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

Abstract

To improve the speed regulating characteristics of continuously variable transmission for agricultural machinery, in order to meet the engineering and technical requirements of precision agriculture and intelligent agriculture, the paper researches and proposes a method combining the analysis of speed regulating characteristics, regression-based correction, and the improved particle swarm optimization (I-PSO) algorithm. First, the paper analyzes the degree of deviation between the linearization degree and the theoretical value of the speed regulating characteristics of the variable-pump constant-motor system of agricultural machinery according to the measurement results of the bench test. Next, the paper corrects the speed regulating characteristics and compares the regression results based on four models. Finally, the paper proposes a design method for the expected speed regulating characteristics of agricultural machinery and it completes the optimization of speed regulating characteristics and the matching of transmission parameters with the I-PSO algorithm. Results indicate that the speed regulating characteristics of the variable-pump constant-motor system show high linearization (with a coefficient of determination of 0.9775). The theoretical and measured values of the speed regulating characteristics have a certain deviation (with a coefficient of determination of 0.8934). Therefore, correcting the speed regulating characteristics of the variable-pimp constant-motor system is highly necessary. In addition, the second reciprocal function model proposed has the highest correction precision (with a coefficient of determination of 0.9978). The I-PSO algorithm is applicable to the design and application of hydro-mechanical continuously variable transmission (HMCVT) for agricultural machinery. The new method proposed can improve the HMCVT’s speed regulating characteristics efficiently and quickly. It also ensures that the speed regulating characteristics are highly consistent with the expected design characteristics (with a mean error of 1.73%). Thus, the research offers a theoretical direction and design basis for the research and development of continuously variable transmission units in agricultural machinery.

Suggested Citation

  • Zhun Cheng & Zhixiong Lu, 2022. "Regression-Based Correction and I-PSO-Based Optimization of HMCVT’s Speed Regulating Characteristics for Agricultural Machinery," Agriculture, MDPI, vol. 12(5), pages 1-18, April.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:5:p:580-:d:798683
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    References listed on IDEAS

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    1. Zhijie Liu & Guoqiang Zhang & Guoping Chu & Hanlin Niu & Yazhou Zhang & Fuzeng Yang, 2021. "Design Matching and Dynamic Performance Test for an HST-Based Drive System of a Hillside Crawler Tractor," Agriculture, MDPI, vol. 11(5), pages 1-21, May.
    2. Yu Qian & Zhun Cheng & Zhixiong Lu & Atila Bueno, 2021. "Bench Testing and Modeling Analysis of Optimum Shifting Point of HMCVT," Complexity, Hindawi, vol. 2021, pages 1-13, May.
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    Cited by:

    1. Mustafa Ucgul & Chung-Liang Chang, 2023. "Design and Application of Agricultural Equipment in Tillage Systems," Agriculture, MDPI, vol. 13(4), pages 1-3, March.
    2. Chin-Hung Kuan & Yungho Leu & Wen-Shin Lin & Chien-Pang Lee, 2022. "The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model," Agriculture, MDPI, vol. 12(8), pages 1-15, July.
    3. Yuting Chen & Zhun Cheng & Yu Qian, 2022. "Research on Wet Clutch Switching Quality in the Shifting Stage of an Agricultural Tractor Transmission System," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    4. Yongqiang Zhang & Zhuang Hu & Min Zhang & Wenting Ba & Ying Wang, 2022. "Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization," IJERPH, MDPI, vol. 19(16), pages 1-11, August.

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