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This paper focuses on the control performance of the loosening and conditioning process of the tobacco industry, which is significant for the temperature, moisture, and toughness of tobacco. Considering that the process is dominated by time delay, different control strategies are designed and compared comprehensively, such as proportional-integral control (PI), active disturbance rejection control (ADRC), delay active disturbance rejection control (DADRC) and Smith predictor active disturbance rejection control (SPADRC). A comprehensive comparison of the tracking performance, disturbance rejection ability, measurement noise rejection performance and ability to handle system uncertainties is conducted. Simulation results show that PI and ADRC obtain better control performance under large-scale variable conditions, and DADRC and SPADRC can obtain better control performance under nominal and small-scale variable conditions. The results can guide the optimization design of control strategies for the loosening and conditioning process of the tobacco industry and other processes dominated by time delay.

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Comparative Control for the Loosening and Conditioning Process of Tobacco Production

  • 会议论文

  • 发表日期：

    2025年

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  • Zhuwen Liu,
  • Hongshuai Hu,
  • Yang Liu,
  • Longfei Yang,
  • Jing Wang,
  • Lei Fan,
  • Xiaoyuan Li,
  • Zhenlong Wu

摘要

This paper focuses on the control performance of the loosening and conditioning process of the tobacco industry, which is significant for the temperature, moisture, and toughness of tobacco. Considering that the process is dominated by time delay, different control strategies are designed and compared comprehensively, such as proportional-integral control (PI), active disturbance rejection control (ADRC), delay active disturbance rejection control (DADRC) and Smith predictor active disturbance rejection control (SPADRC). A comprehensive comparison of the tracking performance, disturbance rejection ability, measurement noise rejection performance and ability to handle system uncertainties is conducted. Simulation results show that PI and ADRC obtain better control performance under large-scale variable conditions, and DADRC and SPADRC can obtain better control performance under nominal and small-scale variable conditions. The results can guide the optimization design of control strategies for the loosening and conditioning process of the tobacco industry and other processes dominated by time delay.