MPC-based control strategy for conflict resolution between vehicles and aircraft on airport
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摘要: 为解决机场场面服务车辆与航空器在滑行道与行车道交叉点频繁发生冲突的问题，提出了一种基于模型预测控制（MPC）的机场自动驾驶车辆与航空器交叉冲突消解方法。分析了机场场面车-机交汇的典型运行场景，明确了车辆在与航空器交叉冲突中需要满足的纵向动力学约束和航空器安全边界要求；在此基础上，提出将航空器及其安全边距经过冲突区域的时段定义为动态红灯时间窗约束，并以车辆能耗最小化和通行效率最大化为优化目标，建立了车辆MPC模型；采用序列二次规划方法对非线性约束优化问题进行滚动求解，实时生成了车辆的最优速度与加速度控制序列；结合天津滨海国际机场的真实场景数据开展仿真试验，并设置了多组随机工况进行对比分析。研究结果表明：在所提出的MPC策略下，车辆能够避免与航空器发生潜在冲突，与常态人工驾驶和理想人工驾驶相比，平均能耗降低14.81%和14.27%，平均通行时间缩短6.48%和5.70%，轨迹平稳性和控制效果均表现出明显优势。研究结果表明，提出的冲突消解控制策略不仅能够提高机场场面车辆运行的安全性与经济性，还为未来机场自动驾驶车辆的应用与推广提供了有效的理论支撑和技术参考。Abstract: To address the frequent conflicts between airport ground service vehicles and aircraft at intersections of taxiways and roadways, a model predictive control (MPC)-based conflict resolution method was proposed for airport autonomous vehicles and aircraft. The typical operation scenarios of ground vehicle-aircraft intersections on the airport were analyzed. The longitudinal dynamic constraints of vehicles and the safety boundary requirements of aircraft in conflict situations were clarified. On this basis, the period when the aircraft and its safety clearance passed through the conflict area was defined as a dynamic red light time window constraint. With the objectives of minimizing vehicle energy consumption and maximizing traffic efficiency, an MPC model for vehicle was established. A sequential quadratic programming method was employed to solve the nonlinear constrained optimization problem in a rolling manner, thereby generating optimal speed and acceleration control sequences for vehicles in real time. Simulation experiments were conducted using real-world scenario data from Tianjin Binhai International Airport. Multiple sets of random operating conditions were also designed for comparative analysis. Research results show that, with the proposed MPC strategy, the vehicle can avoid potential conflicts with aircraft. Compared with conventional human driving and idealized human driving, the average energy consumption is reduced by 14.81% and 14.27%, respectively, and the average travel time is shortened by 6.48% and 5.70%. The trajectory smoothness and control performance are both significantly superior. These findings indicate that the proposed conflict resolution control strategy not only enhances the safety and efficiency of airport ground vehicle operations but also provides practical theoretical support and technical reference for the future application and popularization of autonomous vehicles on airports.
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图 9 有无MPC的平均能耗和驾驶时长对比
Figure 9. Comparison of average energy consumption and travel time with and without MPC
图 10 有无MPC的车辆及航空器时空轨迹
Figure 10. Spatiotemporal trajectories of vehicles and aircraft with and without MPC
表 1 仿真参数
Table 1. Simulation parameters
含义 数值 车辆长度/m 7.45 车辆质量/kg 7 650 车辆最大加速度/（m∙s-2） 3 车辆最小加速度/（m∙s-2） -3 车辆最大速度/（m∙s-1） 8.3 航空器翼展/m 34.09 航空器机长/m 37.57 航空器滑行最大加速度/（m∙s-2） 5 航空器滑行最小加速度/（m∙s-2） -0.98 航空器最大滑行速度/（m∙s-1） 13 航空器侧向净空距离/m 7.5 航空器前向净空距离/m 50 航空器尾流距离/m 30 车辆迎风面积/m2 9 空气阻力系数 0.32 空气密度/（kg∙m-3） 1.23 重力加速度/（m∙s-2） 9.81 滚动阻力系数 0.018 通行效率项权重系数 30 预测步长 100 执行步长 1 时间间隔/s 0.5 驾驶员驾驶风格系数 U（0.9，1.1） 车辆自由行驶时加速度/（m∙s-2） 2 车辆自由行驶时减速度/（m∙s-2） -1 驾驶员观察距离/m 25.45 驾驶员估计航空器远离距离/m 425.57 驾驶员估计航空器接近距离/m 200 驾驶员随机减速概率 0.2 
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