A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles
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摘要: 针对多车协同控制系统中，传统控制算法需要准确获取系统中与驾驶员驾驶行为相关的参数以及与车辆系统动力学相关参数等问题，提出基于数据驱动的自适应动态规划控制算法。以有人与无人驾驶车辆混行的多车协同控制系统为研究对象，通过分析系统的横纵向控制模型，推导出系统状态方程，采用递推数值方法在线逼近最优解，并通过对最优反馈控制矩阵进行优化求解，得到最优控制输入。该算法简化了系统的控制输入参数，仅仅利用V2X通信获得的车辆的前轮转角以及车辆期望的纵向加速度作为控制输入，即可实现无人驾驶车辆的优化控制。基于Carsim和Simulink进行联合仿真测试验证，结果表明，该算法控制参数简单、收敛速度快、控制精度高、适应性强，能够控制无人驾驶车辆在多车系统中保持期望的车速并且与前车保持期望的车间距，同时在任意曲率道路上行驶时与车道中心线之间的横向误差趋于0。Abstract: Traditional model-based control methods need to obtain parameters of driving behaviors of drivers and system dynamics of vehicles in a multi-vehicle cooperative control system. However, these parameters cannot be obtained accurately in actual transport systems. A data-driven adaptive dynamic programming control algorithm is proposed to solve the problem. Under the environment of mixed manned and unmanned vehicles, the horizontal and vertical control models of the multi-vehicle cooperative control system are analyzed to derive its state equation. A recursive numerical method is used to approximate an optimal solution. Optimal control inputs are obtained by optimizing a feedback control matrix. The proposed algorithm simplifies control input parameters of the system. Besides, the optimal control of unmanned vehicles can be realized only using two parameters of basic safety messages of vehicles in real-time as the controller's inputs: steering the angel of the fore wheel and expected longitudinal acceleration. A co-simulation is conducted based on CarSim and Simulink. The results show that the proposed algorithm has simple control parameters, fast convergence speed, high control accuracy, and strong adaptability. It ensures the stability of the multi-vehicle cooperative control system and controls unmanned vehicles in platooning to maintain the desired velocity and desired heading. Moreover, its lateral error between the actual trajectory and expected trajectory tends to zero during driving on the road with arbitrary curvature.
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图 1 人机混驾组成的多车协同控制
Figure 1. Multi-vehicle cooperative control consisting of manned and unmanned vehicles
图 3 数据驱动的自适应动态规划控制算法
Figure 3. Data-driven adaptive dynamic programming-control algorithm
图 4 螺旋型道路下的无人驾驶车横向控制仿真结果
Figure 4. Simulation results of the lateral control of autonomous vehicles on spiral roads
图 5 S型道路下的无人驾驶车横向控制仿真结果
Figure 5. Simulation results of the lateral control of autonomous vehicles on S-Roads
图 6 人机混驾多车协同控制系统行驶轨迹
Figure 6. Trajectory of the multi-vehicle cooperative-control system
图 7 人机混驾车辆车速和车间距随时间的变化关系
Figure 7. Trajectory of the velocity and headway under the lateral and longitudinal integrated control scene
图 8 车辆横向误差和横摆角随时间的变化关系
Figure 8. Trajectory of the lateral error and yaw angle under the lateral and longitudinal integrated control scene
表 1 人机混驾多车协同控制机制符号
Table 1. Notations for the multi-vehicle cooperative control strategy
符号 符号意义 上/下标意义 e1，e2 车辆误差 1-横向误差
2-航向误差$ \dot \varphi $ 横摆角速度/（rad/s) $ h_{i}^{*} $ 制动距离/m i-车辆 $h_{i}$ 第i和第i-1辆车之间的车间距/m i-车辆 $ v_{i}^{*}$ 期望车速（m/s) i-车辆 $ v_{\max }$ 最大车速（m/s) $h_{\text {stop }} $ 有人驾驶车辆制动距离/m K 反馈增益矩阵 $\tau_{i} $ 最小车头时距/s i-车辆 △T 采样时间/s $ \varepsilon $ 阈值 $ v_{0}^{i}$ 初始速度（m/s) i-车辆 $\boldsymbol{A}, \boldsymbol{B} $ 系统状态矩阵 $ \delta$ 车辆的前轮转角/(°) a 车辆期望的纵向加速度（m/s) 
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表 2 人机混驾多车协同控制系统初始化参数设置
Table 2. Initialization parameters of the multi-vehicle cooperative-control system
参数 含义/单位 数值 $h_{1}^{*}, h_{2}^{*} $ 第1/2条直道上的期望车头间距/m 24.56, 29 $ v_{1}^{*}, v_{2}^{*} $ 第1/2条直道上的期望车速（m/s) 15.56, 20 $ v_{\max }$ 最大车速（m/s) 30 $ h_{\text {stop }}$ 有人驾驶车辆制动距离/m 8 $ h_{3}^{*}, h_{5}^{*}$ 第3/5辆无人驾驶车的制动距离/m 9, 9 $ \tau_{3}, \tau_{5}$ 第3/5辆无人驾驶车最小车头时距/s 1, 1 $ \Delta T$ 米样时间/s 0.05 $ \varepsilon$ 阈值 0.01 $v_{0}^{1}, v_{0}^{2} $ 第1/2辆车的初始速度/（m/s) 15.56, 10 $v_{0}^{3}, v_{0}^{4}, v_{0}^{5} $ 第3/4/5辆车的初始速度/(m/s) 8, 6, 4 $h_{0}^{2} $ 第2辆车与第1辆车初始间距/m 20 $ h_{0}^{3}$ 第3辆车与第2辆车初始间距/m 10 $h_{0}^{4}$ 第4辆车与第3辆车初始间距/m 10 h05 第5辆车与第4辆车初始间距/m 10
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