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Research on TD3-Based Distributed Micro-Tillage Traction Bottom Control Strategy

Author

Listed:
  • Guangxiu Ning

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    These authors contributed equally to this work.)

  • Lide Su

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    These authors contributed equally to this work.)

  • Yong Zhang

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Jian Wang

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Caili Gong

    (College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China)

  • Yu Zhou

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

Abstract

Due to its flexibility and versatility, the electric distributed drive micro-tillage chassis can be used more often in the future in Intelligence agriculture scenarios. However, due to the complex working conditions of the agricultural operation environment, it is a challenging task to distribute the torque demand of four wheels reasonably and effectively. In this paper, we propose a drive torque allocation strategy based on deep reinforcement learning to ensure straight-line retention and energy saving, using a distributed electric traction chassis for greenhouses as the research object. The torque assignment strategy can be represented as a Markovian decision process, and the approximate action values and policy functions are obtained through an Actor–Critic network, and the Twin Delayed Deep Deterministic Policy Gradient (TD3) is used to incorporate the vehicle straight-line retention rate into the cumulative reward to reduce energy consumption. The training results under plowing working conditions show that the proposed strategy has a better straight-line retention rate. For typical farming operation conditions, the proposed control strategy significantly improves the energy utilization and reduces the energy by 10.5% and 3.7% compared to the conventional average torque (CAT) distribution strategy and Deep Deterministic Policy Gradient (DDPG) algorithm, respectively. Finally, the real-time executability of the proposed torque distribution strategy is verified by Soil-tank experiments. The TD3 algorithm used in this study has stronger applicability than the traditional control algorithm in dealing with continuous control problems, and provides a research basis for the practical application of intelligent control algorithms in future greenhouse micro-tillage chassis drive control strategies.

Suggested Citation

  • Guangxiu Ning & Lide Su & Yong Zhang & Jian Wang & Caili Gong & Yu Zhou, 2023. "Research on TD3-Based Distributed Micro-Tillage Traction Bottom Control Strategy," Agriculture, MDPI, vol. 13(6), pages 1-17, June.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1263-:d:1173853
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    References listed on IDEAS

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    1. Giorgia Bagagiolo & Giovanni Matranga & Eugenio Cavallo & Niccolò Pampuro, 2022. "Greenhouse Robots: Ultimate Solutions to Improve Automation in Protected Cropping Systems—A Review," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    2. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    3. Zou, Yuan & Liu, Teng & Liu, Dexing & Sun, Fengchun, 2016. "Reinforcement learning-based real-time energy management for a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 171(C), pages 372-382.
    4. Changjie Wu & Xiaolong Tang & Xiaoyan Xu, 2023. "System Design, Analysis, and Control of an Intelligent Vehicle for Transportation in Greenhouse," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
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