Significance of low-level control to string stability under adaptive cruise control: Algorithms, theory and experiments
Significance of low-level control to string stability under adaptive cruise control: Algorithms, theory and experiments
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DOI：
10.1016/j.trc.2022.103697
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Hao Zhou;Anye Zhou;Tienan Li;Danjue Chen;S. Peeta;Jorge A. Laval
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作者：
Hao Zhou;Anye Zhou;Tienan Li;Danjue Chen;S. Peeta;Jorge A. Laval
商用自适应巡航控制系统是双层的：上层规划器决定目标轨迹，下层系统执行目标轨迹。现有的ACCs文献多集中于规划器算法或执行器延迟，而忽略了两者之间的过渡过程，如底层控制设计及其影响。本文试图通过深入研究最近的开源自动驾驶系统Openpilot （OP）的代码库来填补这一空白。Ai，从中我们提取并制定了上层和下层的算法。对于线性ACCs，本文将传递函数分析从仅规划到全控制回路，并研究了慢/快低级控制对整体管柱稳定性（SS）的影响。对于MPC ACCs，它基于优化目标研究其规划特征，并使用ODE方法近似低级影响。我们发现，低水平控制对ACCs的整体SS有显著影响：(i)低水平控制在小频率下破坏SS，并在线性系统的大频率下改善SS， （ii） MPC在振荡过程中具有变化的增益，其中快速的低水平控制通常导致MPC增益的“快-慢”变化过程，这有利于SS，而低水平控制导致“慢-快”变化增益，这破坏了SS， （iii）低水平控制是常见的，因为它们来自于舒适导向的控制增益。从一个“弱”致动器性能或两者兼而有之，和（iv） SS是非常敏感的积分增益在缓慢的低电平控制下的PI和PIF控制器。总体而言，考虑到大拥塞波通常具有小频率和大振幅的特点，该研究建议快速低电平控制以确保车辆SS减少交通拥堵，尽管慢速控制器在提供短而小的前导扰动时可以表现得更好。本文的研究结果得到了数值和实验的验证。在文献中，我们首次在市场汽车上实现自定义ACC算法，并通过仅调整低级控制器在具有随机领导者的开放道路上实现SS。源代码在https://github.com/HaoZhouGT/openpilot共享，以支持任意车辆跟随模型的道路实验，这可能对其他研究感兴趣。
Commercial adaptive cruise control (ACC) systems are bi-level: an upper-level planner decides the target trajectory and the low-level system executes it. Existing literature on ACCs mostly focus on the planner algorithms or the actuator delay, while the transition process between them, e.g. the low-level control design and its impact are often ignored. This paper tries to fill this gap by digging into the codebase of a recent open-source self-driving system, Openpilot (OP), Comma.ai, from which we extract and formulate the algorithms at both the upper and lower levels. For linear ACCs, the paper extends the transfer function analysis from planners only to full control loops and investigates the impact of slow/fast low-level control on the overall string stability (SS). For MPC ACCs, it studies their planning characteristics based its optimization objectives and approximates the low-level impact using an ODE approach.We find that low-level control has a significant impact on the overall SS of ACCs: (i) slow low-level control undermines SS under small frequencies and improves SS given large frequencies for linear systems, (ii) MPC features a varying gain throughout an oscillation, where the fast low-level control typically results in a ‘fast-slow’ changing process of the MPC gain, which benefits the SS, whereas the slow low-level control leads to a ‘slow-fast’ varying gain which undermines the SS, (iii) slow low-level control are common as they arise from comfort-oriented control gains, from a ”weak” actuator performance or both, and (iv) the SS is very sensitive to the integral gain under slow low-level control for both PI and PIF controllers. Overall, the study recommends fast low-level control for ensuring vehicular SS to reduce traffic congestion, considering that large congestion waves usually feature both small frequencies and large amplitudes, although slow controllers could perform even better provided a short and small leader perturbation. The findings of this paper are verified both numerically and experimentally. For the first time in the literature we implement custom ACC algorithms on market cars, and achieve SS on open roads with a random leader by only tuning the low-level controllers. The source code is shared at https://github.com/HaoZhouGT/openpilot to support on-road experiments of arbitrary car-following models, which may be of interest to other studies.