基于MPC的飞机牵引车轨迹跟踪

张军; 黄明辉; 王玥琳; 阳星; 叶敏; 贾永乐

doi:10.13700/j.bh.1001-5965.2023.0431

基于MPC的飞机牵引车轨迹跟踪

doi: 10.13700/j.bh.1001-5965.2023.0431

张军^1, ,,
黄明辉²,
王玥琳³,
阳星⁴,
叶敏¹,
贾永乐⁵

1.
长安大学道路施工技术与装备教育部重点实验室，西安 710064
2.
中国路桥工程有限责任公司，北京 100010
3.
长安大学道路基础设施数字化教育部工程研究中心，西安 710064
4.
西安爱生技术集团有限公司，西安 710065
5.
西安睿诺航空装备有限责任公司，西安 710060

基金项目:

陕西省2022年自然科学基础研究计划(2022JM-249)；陕西省重点研发计划(2023-YBSF-104)；长安大学本科实践类课程建设项目(2025-05)

详细信息

通讯作者:
E-mail：zhangjun@chd.edu.cn

中图分类号: V351.34；TP23
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- 文章访问数: 97
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- 被引次数: 0
出版历程
- 收稿日期: 2023-07-03
- 录用日期: 2024-02-23
- 网络出版日期: 2024-04-09
- 整期出版日期: 2025-09-30

Trajectory tracking for aircraft tug based on MPC

1.
Key Laboratory of Road Construction Technology and Equipment of MOE，Chang’an University，Xi’an 710064，China
2.
China Road & Bridge Corporation，Beijing 100010，China
3.
Engineering Research Center of Ministry of Education for Digital Road Infrastructure，Chang’an University，Xi’an 710064，China
4.
Xi’an ASN Technology Group Co.，Ltd.，Xi’an 710065，China
5.
Xi’an Ruinuo Aviation Equipment Co.，Ltd.，Xi’an 710060，China

Funds:

Natural Science Basic Research Plan in Shaanxi Province of 2022 (2022JM-249); The Key R & D Project in Shaanxi Province (2023-YBSF-104); Undergraduate Practical Course Development Project of Chang’an University (2025-05)

More Information

Corresponding author: E-mail：zhangjun@chd.edu.cn

摘要

摘要:
为能满足物流机场短时间、高频次的快捷飞机牵引需求，提出了基于无人驾驶技术的快速牵引方法。采用“理论建模-算法设计-算例测试和仿真优化-样机实验”的技术路线和方法，以10 t飞机牵引车为对象，构建牵引车的运动学模型，确定牵引车的约束条件和控制量，通过增加防碰撞处理、最小转弯半径和路径平滑的方式改进A^*算法，生成牵引车运动轨迹；设计模型预测控制(MPC)的轨迹跟踪控制器，构建MATLAB/Simulink和ADAMS联合仿真模型，通过轨迹跟踪仿真实验优化MPC的控制参数，并在改造的电传动飞机牵引车样机上开展轨迹跟踪实验。结果表明：改进的A^*算法满足飞机牵引车工作路径规划和最小转弯半径要求，联合仿真方法优化了MPC控制器，在样机上实现了较好的跟踪精度，弯道和直线跟踪误差的标准差分别为0.362 m和0.128 m，实现了飞机牵引车的无人驾驶功能，为智慧物流机场的无人牵引飞机奠定技术基础。
- 电传动飞机牵引车 /
- 无人驾驶技术 /
- 路径规划 /
- 改进A^*算法 /
- 轨迹跟踪 /
- 模型预测控制
Abstract:
To meet the short and high frequency demands of quick aircraft towing at logistics airports, a rapid towing method based on unmanned driving technology is proposed. Using the technical route and method of “theoretical modeling-algorithm design-case test and simulation optimization-prototype experiment”, a kinematic model of the towing vehicle is constructed for a 10-ton aircraft towing vehicle, the vehicle’s constraint conditions and control quantities are determined, and the A^* algorithm is improved by adding collision avoidance processing, minimum turning radius, and path smoothing to generate the motion trajectory of the towing vehicle. A trajectory tracking controller based on model predictive control (MPC) is designed, and a joint simulation model of MATLAB/Simulink and ADAMS is constructed. The control parameters of MPC are optimized through trajectory tracking simulation experiments, and trajectory tracking experiments are carried out on the modified electric aircraft towing vehicle prototype. The results show that the improved A^* algorithm meets the requirements of aircraft towing vehicle work path planning and minimum turning radius, the joint simulation method optimizes the MPC controller, achieves good tracking accuracy on the prototype, with standard deviation of tracking errors for curve paths and straight paths being 0.362 m and 0.128 m respectively, realizes the unmanned driving function of the aircraft towing vehicle, and lays the technical foundation for unmanned aircraft towing at smart logistics airports.
- electric aircraft towing vehicle /
- unmanned driving technology /
- path planning /
- improved A^* algorithm /
- trajectory tracking /
- model predictive control

HTML全文

图 1 牵引车运动学模型

Figure 1. Motion model of tug

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图 2 防碰撞安全范围

Figure 2. Anti-collision safety range

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图 3 MPC控制器组成

Figure 3. MPC controller composition

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图 4 电传动无杆飞机牵引车动力学模型

Figure 4. Dynamics model of electric drive aircraft tug

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图 5 牵引车-飞机系统的机电联合仿真模型

Figure 5. Co-simulation model of tug-aircraft system

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图 6 仿真场景轨迹规划结果

Figure 6. Compared simulated path planning results

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图 7 联合仿真实验结果

Figure 7. Results of joint simulation experiments

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图 8 弯道下牵引车与飞机的连接角度变化

Figure 8. The connection angle change

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图 9 样机的上位机程序

Figure 9. Overall structure of upper computer software

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图 10 无人驾驶飞机牵引车

Figure 10. Autonomous aircraft tug

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图 11 实验场地

Figure 11. Experimental site

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图 12 轨迹跟踪实验结果

Figure 12. Field result of path tracking

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表 1 路径平滑仿真结果

Table 1. Path smoothing simulation results

场景	方法	方差/m²	标准差/m	平滑性
仿真场景1	最小二乘法	1.851	1.360	一般
仿真场景1	梯度下降法	0.015	0.126	好
仿真场景2	最小二乘法	0.026	0.161	一般
仿真场景2	梯度下降法	0.012	0.111	好

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表 2 不同控制参数下的MPC控制效果对比

Table 2. Compared results at different MPC control parameters

参数组合	$ {N_{\rm c}} $	$ {N_{\rm p}} $	标准差/m	仿真时间/s
1	30	60	0.267	6.95
2	30	50	0.247	6.39
3	30	40	0.231	6.23
4	30	30	0.218	5.92
5	20	20	0.204	5.07
6	10	10	0.182	3.73
7	5	10	0.183	2.83
8	5	5	0.157	2.78

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表 3 联合仿真轨迹结果

Table 3. Joint simulation trajectory results

场景	方差/m²	标准差/m
仿真场景1	0.021	0.146
仿真场景2	0.016	0.128

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表 4 轨迹跟踪误差分析

Table 4. Track tracking error analysis

场景	方差/m²	标准差/m
实验场景1	0.131	0.362
实验场景2	0.016	0.128

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