Current status and progress of path tracking control of mining articulated vehicles
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摘要: 鉸接式車輛的路徑跟蹤控制是礦山自動化領域中的關鍵技術，而數學模型和路徑跟蹤控制方法是鉸接式車輛路徑跟蹤控制中的兩項重要研究內容。在數學模型研究中，鉸接式車輛的無側滑經典運動學模型較為適合作為低速路徑跟蹤控制的參考模型，而有側滑運動學模型作為參考模型時則可能導致側滑加劇。此外基于牛頓–歐拉法建立的鉸接式車輛四自由度動力學模型原則上滿足路徑跟蹤控制的需求，但是還需要解決當前的四自由度模型無法同時反映瞬態轉向特性和穩態轉向特性的問題。在路徑跟蹤控制方法研究中，反饋線性化控制、最優控制、滑模控制等無前饋信息的控制方法無法有效解決鉸接式車輛跟蹤存在較大幅度曲率突變的參考路徑時誤差較大的問題，前饋–反饋控制可以用于解決上述問題，但是在參考路徑具有不同幅度的曲率突變時需要解決自動調整預瞄距離的問題，而模型預測控制，尤其是非線性模型預測控制，可以更加有效地利用前饋信息，且不需要考慮預瞄距離的設置，從而可以有效提高鉸接式車輛跟蹤存在較大幅度曲率突變的參考路徑時的精確性。此外，對于基于非線性模型預測控制的鉸接式車輛路徑跟蹤控制，還需深化三個方面的研究。首先，該控制方法仍然存在誤差最大值隨參考速度增大而增加的趨勢。其次，目前該控制方法以運動學模型作為預測模型，無法解決鉸接式車輛以較高的參考速度運行時側向速度導致的精確性下降和安全性惡化的問題。最后，還需對這種控制方法進行實時性方面的優化研究。Abstract: Path tracking control of articulated vehicles is a focus in the field of mine automation. Mathematical models and path tracking control methods are two key focal points of research in this area. For mathematical models of articulated vehicles, the classic kinematics model without side-slip is suitable as a reference model for low-speed autonomous driving control. However, when this model is used as the reference model, it may lead to an intensification of sliding. In any event, the four-degree-of-freedom dynamic model of articulated vehicles based on the Newton–Euler method meets the requirements of autonomous driving control in principle. However, this model cannot reflect both transient and steady steering characteristics. In the research of path tracking control methods, feedback linearization control, optimal control, sliding mode control, and other control methods without feedforward information cannot effectively solve the problem of a large error when vehicle tracking a reference path with large abrupt changes in curvature. Feedforward–feedback control can be used to solve the above problem, but when the reference path has diverse amplitudes of abrupt changes in curvature, it is necessary to automatically adjust the preview distance. Model predictive control, especially nonlinear model predictive control, can use feedforward information more effectively and does not need to consider the setting of the preview distance. This way, when the articulated vehicle tracks a reference path with large abrupt changes in curvature, accuracy can be effectively improved. Additionally, for the path tracking control of articulated vehicles based on nonlinear model predictive control, three aspects of research need to be deepened. First, for this control method, there is still a trend that the maximum value of the error increases as the reference velocity increases. Second, currently, this control method uses the kinematics model as the prediction model, so it cannot solve the twin problems of reduced accuracy and worsened safety, caused by the lateral velocity when the articulated vehicle runs at a higher reference velocity. Finally, real-time optimization research on this control method is needed.
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Key words:
- mining /
- vehicle /
- articulated /
- unmanned driving /
- path tracking /
- current status /
- progress
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圖 4 NMPC和NEMPC的仿真結果局部放大
Figure 4. Partial enlargement of simulation results of NMPC and NEMPC
圖 8 NMPC的誤差最大值隨參考速度變化趨勢
Figure 8. Change trend in maximum error of NMPC with reference velocity changes
表 1 鉸接式車輛和控制器參數
Table 1. Parameters of the articulated vehicle and controllers
Parameter Value Distance between articulated point to
the front axle /m1.68 Distance between articulated point to
the rear axle /m3.44 Control period /s 0.05 Prediction horizon 40 Control horizon 1 Weight of errors 0.01I（I is unit matrix） Weight of control input increment 0.0005I（I is unit matrix） 
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