参数自优化的有人与无人车辆编队鲁棒模型预测控制

doi:10.12382/bgxb.2022.0650

摘要/Abstract

摘要：

为解决有人与无人车辆编队中,有人领航车紧急加减速和紧急转向控制输入对无人车跟踪控制的扰动问题,设计了一种参数自优化的有人与无人车辆编队鲁棒模型预测控制算法。通过采集分析历史数据确定控制器扰动的噪声极值,并经过适度放缩得到其鲁棒边界。设计抑制该扰动的局部反馈鲁棒控制器,并通过贝叶斯优化的方法实现鲁棒边界等控制器参数自优化。基于混合整数线性优化的方法预测有人领航车未来轨迹,并设计鲁棒模型预测控制器实现无人车对有人领航车的跟踪控制。仿真和实车试验结果表明:所设计的鲁棒模型预测控制器在跟踪精度方面相比于传统模型预测控制器有明显的提升;同时该控制器有效地抵抗了来自有人领航车紧急加减速和紧急转向控制输入、无人跟随车系统模型不确定性和外部环境的扰动,振荡情况明显改善,提高了系统的鲁棒性。

关键词: 有人与无人车辆编队, 领航跟踪控制, 鲁棒模型预测控制, 贝叶斯优化

Abstract:

To solve the problem of disturbances in unmanned vehicle tracking control caused by the emergency acceleration, deceleration and steering control input of the manned leading vehicle in a formation of manned and unmanned vehicles, a parameter self-optimizing robust model predictive controller is designed. The noise extremum of the disturbances is determined by collecting and analyzing the historical data, which is scaled moderately to obtain a robust boundary. A local feedback robust controller is designed to restrain the disturbances, and the controller’s parameters are automatically optimized using the Bayesian optimization algorithm. The mixed-integer linear optimization method is used to predict the trajectory of the leading vehicle, and a robust model predictive controller is proposed to track the leading vehicle using an unmanned vehicle. The simulation and experimental results show that the robust model predictive controller designed in this paper has a significant improvement in tracking accuracy compared with traditional controllers. The controller also effectively restrains the disturbances caused by emergency acceleration, deceleration and steering control input of the manned leading vehicle, model uncertainty of unmanned tracking vehicle and other external factors. Vibration is obviously suppressed, and the robustness of the system is enhanced.

Key words: manned and unmanned vehicle formation, piloting and tracking control, robust model predictive control, Bayesian optimization

中图分类号:

U469.79

宋佳睿, 陶刚, 李德润, 臧政, 吴绍斌, 龚建伟. 参数自优化的有人与无人车辆编队鲁棒模型预测控制[J]. 兵工学报, 2023, 44(1): 84-97.

SONG Jiarui, TAO Gang, LI Derun, ZANG Zheng, WU Shaobin, GONG Jianwei. Robust Model Predictive Control for Manned and Unmanned Vehicle Formation Based on Parameter Self-Optimization[J]. Acta Armamentarii, 2023, 44(1): 84-97.

图/表 22

图1 总体框架图

Fig.1 Overall frame diagram

图2 领航跟踪几何模型

Fig.2 Cooperative control geometry model

图3 轮式车辆运动学模型

Fig.3 Kinematic model of a wheeled vehicle

图4 履带车辆运动学模型

Fig.4 Kinematic model of a tracked vehicle

图5 车辆轨迹树示意图

Fig.5 Vehicle trajectory tree diagram

图6 轨迹树一步扩展算法伪代码

Fig.6 Pseudo code of one-step extension algorithm of the trajectory tree

图7 模型一步预测误差

Fig.7 Frame diagram of the model predictive co-controller

图8 鲁棒不变集计算流程伪代码

Fig.8 Computation flow pseudo code of the robust invariant set

图9 基于贝叶斯优化的参数自优化框架图

Fig.9 Frame diagram of parameter self-optimization based on Bayesian optimization

图10 贝叶斯优化算法伪代码表

Fig.10 Pseudo code of the Bayesian optimization algorithm

表1 各平台实验参数

Table 1 Parameters of each platform

实验平台	参数	数值
	长度×宽度×高度/mm	4900×2900×1800
	轴距/mm	2800
	后轴距离/mm	1450
有人车	整车质量/kg	1500
	前轮最大转角/(°)	30
	最大纵向加速度/(m·s^-2)	3.0
	最大纵向减速度/(m·s^-2)	3.0
	长度×宽度×高度/mm	2600×1300×960
	质量/kg	1500
无人车	主动轮半径/mm	140
	车辆离地高度/mm	460
	履带中心距/mm	1100

图11 ROS-VREP联合仿真车辆模型

Fig.11 ROS-VREP co-simulation vehicle model

图12 ROS-VREP联合仿真场景

Fig.12 ROS-VREP co-simulation environment

图13 有人车与无人车参考路径

Fig.13 Reference paths for manned and unmanned vehicles

图14 贝叶斯优化结果

Fig.14 Bayesian optimization results

表2 控制器参数

Table 2 Parameters of the controller

参数	数值
横向偏差权重系数	5.11
航向偏差权重系数	13.77
控制量权重系数	30.10
控制量变化率权重系数	502.66
距离偏差权重系数	6.79
鲁棒边界放缩系数	-0.57

图15 参考路径和场地图

Fig.15 Reference paths and field map

图16 领航跟踪控制效果

Fig.16 Piloting and tracking control effects

表3 仿真实验结果统计

Table 3 Simulation results

无人车		间距误差/m	横向偏差/m	航向偏差/(°)
1	MPC	0.530±1.127	0.162±0.156	2.371±2.180
	RMPC	0.589±1.256	0.134±0.116	2.090±1.769
2	MPC	0.820±1.661	0.171±0.172	2.333±2.431
	RMPC	0.869±1.745	0.151±0.132	2.214±1.989

表4 车辆参数

Table 4 Vehicle parameters

车辆参数	数值
长度×宽度×高度/mm	4740×2170×2380
轴距/mm	3300
整车整备质量/kg	3250
前轮最大转向角/(°)	32
最大纵向加速度/(m·s^-2)	3.0
最大纵向减速度/(m·s^-2)	3.0
离地高度/mm	410
车辆接近角/(°)	60
车辆离去角/(°)	45

图17 领航跟踪实车试验跟踪结果

Fig.17 Piloting and tracking real vehicle test tracking results

表5 实车试验结果统计

Table 5 Experimental results

控制器	间距误差/m	横向偏差/m	航向偏差/(°)
MPC	3.878±2.719	0.536±0.208	3.314±2.101
RMPC	3.724±2.034	0.390±0.176	3.180±2.050

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