基于状态空间建模的智能农机模型辨识与柔化控制
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

2020年度教育部-中国移动科研基金项目(MCM2020—J-2)


State-space Modeling and Identification of Intelligent Agricultural Machinery and Flexible LQR Control
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    农机行驶速度切变通常会导致自动导航系统控制精度和稳定性下降、导向轮摆动幅度增大、影响作业效果等问题。本文从建模和控制的角度对上述问题进行了研究改进,设计了智能控制器、转角传感器、RTK基准站等关键设备,建立包含横摆动力学的状态空间模型,实现了能够适应较大速度变化的状态反馈控制器。并以东风DF1004-2型轮式拖拉机为实验平台,进行了智能化改装和实验验证。智能农机路径跟踪控制一般假设速度为常数,基于运动学模型设计反馈控制或者追踪控制,由于没有对横摆角速率状态加以利用,偏差纠正收敛较慢,偏差较大,且速度发生切变时,导向轮调整幅度变大,精度和稳定性均会下降。通过对农机进行系统辨识,建立农机横摆运动的动力学模型,然后运用辨识出的动力学模型设计基于LQR算法的柔化反馈控制,较好地解决了上述问题。在农田中实验结果表明,农机自主导航系统在无速度切变时控制精度达到0.03m,速度发生切变时为0.05m,未发生失稳现象,能够满足农机日常生产工作需求。

    Abstract:

    For autonomous navigation systems, when the agricultural machinery changes speed, it usually leads to the worsening of control precision and stability. As the Beidou satellite navigation technology and the MEMS inertial sensor technology become increasingly mature, the research and development of autonomous navigation system of agricultural machinery have been greatly promoted. The Dongfeng agricultural tractor DF1004-2 was modified to be an experimental platform, and the crucial devices such as intelligent controller, wheel angle sensor, RTK base station were designed and developed for the tractor’s intelligent modification. Moreover, the state space model, including yaw rate state was established to achieve a stable feedback controller that adapted to large speed changes. Traditionally, the path tracking control of intelligent tractors assumed speed to be constant and designed controllers with a kinematic model or used pursuit strategy without a model. For these controllers, the convergence speed can be slow due to lack of yaw rate information, moreover, when speed switched, the steering magnitude of front wheel grew, which decreased the control accuracy and stability. The state-space model with yaw dynamics was established via system identification, and then a flexible LQR control strategy was designed based on the state-space model, which provided a solution to the challenge. Experimental results from agricultural fields showed that the control precision was 3cm without speed switch, and 5cm with speed switch, which met the production requirements of agricultural machinery.

    参考文献
    相似文献
    引证文献
引用本文

袁洪良,郭锐,薛梦琦,卢潇潇,杨浚宇,徐立鸿.基于状态空间建模的智能农机模型辨识与柔化控制[J].农业机械学报,2022,53(10):405-411. YUAN Hongliang, GUO Rui, XUE Mengqi, LU Xiaoxiao, YANG Junyu, XU Lihong. State-space Modeling and Identification of Intelligent Agricultural Machinery and Flexible LQR Control[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(10):405-411.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-12-05
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2022-03-28
  • 出版日期: