基于預瞄距離的地下礦用鉸接車路徑跟蹤預測控制

孟宇; 甘鑫; 白國星

doi:10.13374/j.issn2095-9389.2019.05.013

基于預瞄距離的地下礦用鉸接車路徑跟蹤預測控制

doi: 10.13374/j.issn2095-9389.2019.05.013

孟宇^{1, 2, ,},
甘鑫¹,
白國星¹

1.
北京科技大學機械工程學院, 北京 100083
2.
北京科技大學人工智能研究院, 北京 100083

基金項目:

國家重點研發計劃課題資助項目 2018YFC0604403

國家重點研發計劃課題資助項目 2016YFC0802905

中央高校基本科研業務資助項目 FRF-TP-17-010A2

國家高技術研究發展計劃(863計劃)資助項目 2011AA060408

詳細信息

通訊作者:
孟宇, E-mail: myu@ustb.edu.cn

中圖分類號: TP273.1
計量
- 文章訪問數: 1121
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- 被引次數: 0
出版歷程
- 收稿日期: 2018-04-13
- 刊出日期: 2019-05-01

Path following control of underground mining articulated vehicle based on the preview control method

MENG Yu^{1, 2
, ,},
GAN Xin¹,
BAI Guo-xing¹

1.
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: MENG Yu, E-mail: myu@ustb.edu.cn

摘要

摘要: 礦用車輛無人駕駛是實現礦山無人化開采的關鍵技術, 而路徑跟蹤控制是無人駕駛系統的核心技術之一.路徑跟蹤控制系統是多變量、多約束系統, 采用傳統方法在多約束條件下存在執行器飽和等問題.針對上述問題, 本文引入模型預測控制方法, 通過考慮車輛的姿態與位置之間的關系, 以跟蹤路徑的橫向偏差最小化和車輛的航向角偏差最小化為目標對預測控制的目標函數進行優化, 以獲得車輛速度和鉸接角度的最優控制量, 實現對多變量、多約束系統的求解.針對模型預測控制算法不能提前判斷道路曲率突變而導致跟蹤超調的問題, 提出基于預瞄距離的控制方法, 通過提前判斷道路突變信息, 提高車輛路徑跟蹤精確性和穩定性.使用Matlab/Adams仿真軟件進行對比仿真試驗, 結果表明: 使用模型預測跟蹤控制器能夠解決多變量、多約束系統控制問題, 有效防止執行器飽和; 而使用基于預瞄距離的模型預測跟蹤控制器能夠使車輛的橫向位置偏差保持在±0.04 m, 航向角偏差保持在±1.8°范圍內, 相較于改進前的控制器, 其橫向位置偏差減少了80.9%, 航向角偏差減少了59.1%, 證明改進后的控制器具有更好的橫向穩定性和精確性.
- 鉸接式車輛 /
- 路徑跟蹤 /
- 運動控制 /
- 模型預測控制 /
- 預瞄控制
Abstract: Due to the narrow roadway and poor working environment, underground mines pose a threat to the safety of vehicle drivers. The realization of automatic driving of underground mine vehicles can improve mining automation and intelligence and ensure safety of workers, and it can significantly increase mining and exploitation efficiency. Automatic driving of underground mining vehicles requires the technologies of location, communication, navigation, and path following control. Automatic driving of mining vehicles is the ultimate approach of autonomous navigation and auto driving, while path following control system is one of the core technologies of the autopilot system. The path following control system is a multi-variable, multi-constraint system. There are optimization problems under multiple constraints as well as challenges such as actuator saturation during the control process. To solve the above problems, a model predictive control method was introduced in this paper. By considering the relationship between the position and situation of the vehicle, the objective function of the predictive control was optimized by minimizing the lateral deviation of the following path and the heading angle deviation of the vehicle. Therefore, the optimal controls of vehicle speed and articulation angle were obtained, and the problem of multi-variable and multi-constraint system was solved. For the tracking overshoot problem caused by the inability of determining sudden changes of road curvature in the model predictive control strategy, a control method based on preview distance was proposed; thus, the vehicle path following control accuracy and stability was improved through the advance judgment of road mutation information. Matlab/Adams simulation software was used to perform a comparison simulation test. The results show that the model predictive following controller is capable of solving the control problem in multi-variable, multi-constrained system and effectively prevent the actuator saturation. Moreover, the model predictive following control strategy based on the preview distance keeps the horizontal deviation of the vehicle within ±0.04 m and the heading angle deviation within ±1.8°. Compared with the controller before improvement, the lateral position deviation is reduced by 80.9%, and the heading angle deviation is reduced by 59.1%; this proves that the improved controller has better lateral stability and accuracy.
- articulated vehicle /
- path following /
- motion control /
- model predictive control /
- preview control

HTML全文

圖 1 鉸接車轉向模型

Figure 1. Articulated vehicle model

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圖 2 MPC路徑跟蹤控制原理圖

Figure 2. Schematic of vehicle path tracking control of MPC

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圖 3 MPC控制器路徑跟蹤流程簡圖

Figure 3. Block diagram of MPC path following the controller

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圖 4 路徑超調示意圖

Figure 4. Path overshoot diagram

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圖 5 基于預瞄距離的MPC控制器路徑跟蹤控制流程圖

Figure 5. Block diagram of MPC path following control based on preview distance

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圖 6 聯合仿真示意圖

Figure 6. Block diagram of unity simulation scheme

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圖 7 基于Adams的35 t鉸接式車輛模型

Figure 7. 35 t articulated vehicle model based on Adams

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圖 8 誤差參數示意圖

Figure 8. Definition of error parameters

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圖 9 “S”型曲線

Figure 9. Definition of "S" path

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圖 10 純MPC控制器與基于預瞄距離MPC控制器對曲率半徑20 m路徑跟蹤效果對比

Figure 10. Comparison of tracking results based on a radius path of curvature of 20 m between the pure MPC controller and the preview distance-based MPC controller

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圖 11 車輛速度變化曲線

Figure 11. Curves of vehicle running speed

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圖 12 車輛鉸接角速度變化曲線

Figure 12. Curves of vehicle articulated angular velocity

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圖 13 車輛鉸接角度變化曲線

Figure 13. Curves of vehicle articulation angle

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圖 14 車輛路徑跟蹤的橫向位置偏差

Figure 14. Lateral positional deviation of vehicle path tracking

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圖 15 車輛路徑跟蹤的航向角偏差

Figure 15. Heading angle deviation of vehicle path tracking

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圖 16 純MPC控制器與基于預瞄距離MPC控制器對曲率半徑10 m路徑跟蹤效果對比

Figure 16. Comparison of tracking results based on a radius path of curvature of 10 m between the pure-MPC controller and the preview distance MPC controller

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圖 17 車輛速度變化曲線

Figure 17. Curve of vehicle running speed

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圖 18 車輛鉸接角速度變化曲線

Figure 18. Curve of vehicle articulated angular velocity

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圖 19 車輛鉸接角度變化曲線

Figure 19. Curve of vehicle articulation angle

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圖 20 車輛路徑跟蹤的橫向位置偏差

Figure 20. Lateral positional deviation of vehicle path tracking

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圖 21 車輛路徑跟蹤的航向角偏差

Figure 21. Heading angle deviation of vehicle path tracking

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