亚洲一区欧美在线,日韩欧美视频免费观看,色戒的三场床戏分别是在几段,欧美日韩国产在线人成

基于狀態(tài)空間建模的智能農(nóng)機模型辨識與柔化控制
CSTR:
作者:
作者單位:

作者簡介:

通訊作者:

中圖分類號:

基金項目:

2020年度教育部-中國移動科研基金項目(MCM2020—J-2)


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

Fund Project:

  • 摘要
  • |
  • 圖/表
  • |
  • 訪問統(tǒng)計
  • |
  • 參考文獻
  • |
  • 相似文獻
  • |
  • 引證文獻
  • |
  • 資源附件
  • |
  • 文章評論
    摘要:

    農(nóng)機行駛速度切變通常會導(dǎo)致自動導(dǎo)航系統(tǒng)控制精度和穩(wěn)定性下降、導(dǎo)向輪擺動幅度增大、影響作業(yè)效果等問題。本文從建模和控制的角度對上述問題進行了研究改進,設(shè)計了智能控制器、轉(zhuǎn)角傳感器、RTK基準站等關(guān)鍵設(shè)備,建立包含橫擺動力學(xué)的狀態(tài)空間模型,實現(xiàn)了能夠適應(yīng)較大速度變化的狀態(tài)反饋控制器。并以東風(fēng)DF1004-2型輪式拖拉機為實驗平臺,進行了智能化改裝和實驗驗證。智能農(nóng)機路徑跟蹤控制一般假設(shè)速度為常數(shù),基于運動學(xué)模型設(shè)計反饋控制或者追蹤控制,由于沒有對橫擺角速率狀態(tài)加以利用,偏差糾正收斂較慢,偏差較大,且速度發(fā)生切變時,導(dǎo)向輪調(diào)整幅度變大,精度和穩(wěn)定性均會下降。通過對農(nóng)機進行系統(tǒng)辨識,建立農(nóng)機橫擺運動的動力學(xué)模型,然后運用辨識出的動力學(xué)模型設(shè)計基于LQR算法的柔化反饋控制,較好地解決了上述問題。在農(nóng)田中實驗結(jié)果表明,農(nóng)機自主導(dǎo)航系統(tǒng)在無速度切變時控制精度達到0.03m,速度發(fā)生切變時為0.05m,未發(fā)生失穩(wěn)現(xiàn)象,能夠滿足農(nóng)機日常生產(chǎn)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.

    參考文獻
    相似文獻
    引證文獻
引用本文

袁洪良,郭銳,薛夢琦,盧瀟瀟,楊浚宇,徐立鴻.基于狀態(tài)空間建模的智能農(nóng)機模型辨識與柔化控制[J].農(nóng)業(yè)機械學(xué)報,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.

復(fù)制
分享
文章指標
  • 點擊次數(shù):
  • 下載次數(shù):
  • HTML閱讀次數(shù):
  • 引用次數(shù):
歷史
  • 收稿日期:2021-12-05
  • 最后修改日期:
  • 錄用日期:
  • 在線發(fā)布日期: 2022-03-28
  • 出版日期:
文章二維碼