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無人駕駛車輛路徑跟蹤控制研究現狀

Current status of path tracking control of unmanned driving vehicles

  • 摘要: 近年來路徑跟蹤控制的發展十分迅猛,研究者們發表了大量的研究成果。考慮到在相同或相近工況下的路徑跟蹤控制存在一些共性的技術問題與解決思路,從低速路徑跟蹤控制和高速路徑跟蹤控制兩個角度對近年來的研究成果進行了回顧。在關于低速路徑跟蹤控制的研究工作中,研究者們較為重視前輪轉角速度約束等系統約束對路徑跟蹤精確性的影響。目前減少系統約束影響的方法包括在規劃參考路徑時將系統約束納入考慮,采用預瞄控制使控制器提前響應,以及采用線性模型預測控制(LMPC)或非線性模型預測控制(NMPC)等模型預測控制方法作為路徑跟蹤控制方法等。考慮到NMPC既能減少系統約束的影響,又無需人為設置預瞄距離,且對定位誤差等擾動因素具有較強的魯棒性,加之低速路徑跟蹤控制對實時性的需求較低,因此可以認為NMPC能夠滿足低速路徑跟蹤控制的絕大多數需求。高速路徑跟蹤控制在受系統約束影響之外,還面臨著較高車速帶來的行駛穩定性不足問題的挑戰,因此常采用能夠將動力學層面的復雜系統約束納入考慮且計算成本較低的LMPC作為路徑跟蹤控制方法。不過僅采用動力學層面的LMPC控制方法無法完全解決高速路徑跟蹤控制中路徑跟蹤精確性和車輛行駛穩定性之間存在耦合的問題,目前常見的解決思路是在路徑跟蹤控制中加入額外的速度調節或權重分配模塊。此外,在高速路徑跟蹤控制中,地面附著系數等環境參數的影響也較大,因此地面附著系數等環境參數的估算也成為了高速路徑跟蹤控制領域的重要研究方向。

     

    Abstract: Path tracking control is a key technology in the hierarchical unmanned driving system. Its function is to control the vehicle so that it drives along the reference path given by the path planning system. The information such as the position and posture of the vehicle required for path tracking control is provided by the perception and positioning system. In recent years, the development of path tracking control has been very rapid, and researchers have published considerable research. As there are some common technical problems and solutions in path tracking control under the same or similar scenarios, recent research results are reviewed from the perspective of both low-speed and high-speed path tracking control. In the research of low-speed path tracking control, researchers pay more attention to the influence of system constraints on the accuracy of path tracking such as front-wheel angle speed. At present, methods to reduce the influence of system constraints include: (1) taking the system constraints into consideration when planning a reference path; (2) using preview control to make the controller respond early; and (3) using model predictive control methods, such as linear model predictive control (LMPC) or non-linear model predictive control (NMPC), as path tracking control methods. NMPC can reduce the impact of system constraints and does not need manual setting of preview distance. It has strong resistance to disturbance factors such as positioning errors. Since low-speed path tracking control has low real-time requirements, it can be considered that NMPC can meet most needs of low-speed path tracking control. High-speed path tracking control, in addition to being affected by system constraints, is also challenged by insufficient driving stability caused by higher vehicle speeds. Therefore, LMPC, which can take the dynamics-level complex system constraints into account, has a lower computational cost. It is often used as the path tracking control method. However, due to high-speed path tracking control, there is a coupling relationship between path tracking accuracy and vehicle driving stability. The use of dynamics-level LMPC or other dynamics-level control methods cannot completely solve the problem caused by this coupling relationship. The current common solution is to add an extra speed adjustment module or weight distribution module to path tracking control. Additionally, in high-speed path tracking control, the influence of environmental parameters, such as ground adhesion coefficient, is also greater. Hence, the estimation of environmental parameters, such as ground adhesion coefficient, has also become an important research direction in the field of high-speed path tracking control.

     

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