Robust Model Predictive Control for Manned and Unmanned Vehicle Formation Based on Parameter Self-Optimization

doi:10.12382/bgxb.2022.0650

Abstract

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

CLC Number:

U469.79

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.

Figures/Tables 22

References 25

[1]	范博洋, 赵高鹏, 薄煜明, 等. 多目标空地异构无人系统协同任务分配方法[J/OL]. 兵工学报, 2022(2022-07-05).https://doi.org/10.12382/byxb.2022.0095. doi: https://doi.org/10.12382/byxb.2022.0095
	FAN B Y, ZHAO G P, BO Y M, et al. Collaborative task allocation method for multi-target aerial-ground heterogeneous unmanned system[J/OL]. Acta Armamentarii, 2022(2022-07-05).https://doi.org/10.12382/byxb.2022.0095. (in Chinese) doi: https://doi.org/10.12382/byxb.2022.0095
[2]	ZHANG J L, YAN J G, ZHANG P. Multi-UAV formation control based on a novel back-stepping approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(3): 2437-2448. doi: 10.1109/TVT.2020.2964847 URL
[3]	贾永楠, 李擎. 多机器人编队控制研究进展[J]. 工程科学学报, 2018, 40(8):893-900.
	JIA Y N, LI Q. Research deve1opment of mu1ti-robot formation contro1[J]. Chinese Journal of Engineering, 2018, 40(8): 893-900. (in Chinese)
[4]	FERRARA A, PISU P. Minimum sensor second-order sliding mode longitudinal control of passenger vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2004, 5(1): 20-32. doi: 10.1109/TITS.2004.825080 URL
[5]	WU Y J, LI S B E, ZHENG Y, Hedrick J K. Distributed sliding mode control for multi-vehicle systems with positive definite topologies[C]// Proceedings of 2016 Conference on Decision and Control. Las Vegas, NV,US:IEEE, 2016: 5213-5219.
[6]	PLOEG J, SHUKLA D P, WOUW N V D, et al. Controller synthesis for string stability of vehicle platoons[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 15(2): 854-865. doi: 10.1109/TITS.2013.2291493 URL
[7]	钱殿伟. 基于模糊滑模的多机器人系统编队控制[J]. 智能系统学报, 2016, 11(5):641-647.
	QIAN D W. Formation control of multi-robot systems in a fuzzy sliding mode[J]. CAAI Transactions on Intelligent Systems, 2016, 11(5):641-647. (in Chinese)
[8]	高瑜隆. 多智能体系统的分布式模型预测控制[D]. 北京: 北京理工大学, 2016.
	GAO Y L. Distributed model predictive control of multi-agent systems[D]. Beijing: Beijing Institute of Technology, 2016. (in Chinese)
[9]	ZHENG Y, LI S B E, LI K Q, et al. Distributed model predictive control for heterogeneous vehicle platoons under unidirectional topologies[J]. IEEE Transactions on Control Systems Technology, 2017, 25(3): 899-910. doi: 10.1109/TCST.2016.2594588 URL
[10]	GAO F, HU X S, LI S B E, et al. Distributed adaptive sliding mode control of vehicular platoon with uncertain interaction topology[J]. IEEE Transactions on Industrial Electronics, 2018, 65(8): 6352-6361. doi: 10.1109/TIE.2017.2787574 URL
[11]	SHLADOVER S E, NOWAKOWSKI C, LU X Y. Using cooperative adaptive cruise control (CACC) to form high-performance vehicle streams. definitions literature review and operational concept alternatives:UCB-ITS-PRR-2014-7[R]. UC Berkeley,CA,US: California Partners for Advanced Transportation Technology, 2018.
[12]	ZHENG Y, LI S B E, WANG J Q, et al. Stability and scalability of homogeneous vehicular platoon: Study on the influence of information flow topologies[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 17(1): 14-26. doi: 10.1109/TITS.2015.2402153 URL
[13]	SWAROOP D, HEDRICK J K, CHIEN C C, et al. A comparison of spacing and headway control laws for automatically controlled vehicles[J]. Vehicle System Dynamics, 1994, 23(1): 597-625. doi: 10.1080/00423119408969077 URL
[14]	鲁若宇, 胡杰, 陈瑞楠, 等. 基于DMPC的智能汽车协同式自适应巡航控制[J]. 汽车工程, 2021, 43(8): 1177-1186.
	LU R Y, HU J, CHEN R N, et al. Cooperative adaptive cruise control of intelligent vehicles based on DMPC[J]. Automotive Engineering, 2021, 43(8):1177-1186. (in Chinese)
[15]	ZHAI C J, LIU Y G, LUO F. A switched control strategy of heterogeneous vehicle platoon for multiple objectives with state constraints[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(5): 1883-1896. doi: 10.1109/TITS.2018.2841980 URL
[16]	OZKAN M F, MA Y. Fuel-economical distributed model predictive control for heavy-duty truck platoon[C]// Proceedings of 2021 IEEE International Intelligent Transportation Systems Conference. Indianapolis, IN, US:IEEE, 2021: 1919-1926.
[17]	李永福, 邬昌强, 朱浩, 等. 考虑车辆跟驰作用和通信时延的网联车辆队列轨迹跟踪控制[J]. 自动化学报, 2021, 47(9): 2264-2275.
	LI Y F, WU C Q, ZHU H, et al. Trajectory tracking control for connected vehicle platoon considering car-following interactions and time delays[J]. Acta Automatica Sinica, 2021, 47(9): 2264-2275. (in Chinese)
[18]	孙中奇. 轮式机器人跟踪控制研究:模型预测控制方法[D]. 北京: 北京理工大学, 2018.
	SUN Z Q. Study on tracking control of wheeled mobile robots: model predictive control approach[D]. Beijing: Beijing Institute of Technology, 2018. (in Chinese)
[19]	ZAMBELLI M, FERRARA A. Robustified distributed model predictive control for coherence and energy efficiency-aware platooning[C]// Proceedings of 2019 American Control Conference. Philadelphia, PA, US:IEEE, 2019: 527-532.
[20]	ZHUANG W C, XU L W, YIN G D. Robust cooperative control of multiple autonomous vehicles for platoon formation considering parameter uncertainties[J]. Automotive Innovation, 2020, 3(1): 88-100. doi: 10.1007/s42154-020-00093-2 URL
[21]	MENG Z, HU C F, CAO L, et al. Distributed model predictive control for multiple unmanned ground vehicles formation with packet loss[C]// Proceedings of 2019 Chinese Control Conference. Guangzhou, China:IEEE, 2019: 6124-6129.
[22]	邓海鹏, 麻斌, 赵海光, 等. 自主驾驶车辆紧急避障的路径规划与轨迹跟踪控制[J]. 兵工学报, 2020, 41(3): 585-594. doi: 10.3969/j.issn.1000-1093.2020.03.020
	DENG H P, MA B, ZHAO H G, et al. Path planning and tracking control of autonomous vehicle for obstacle avoidance[J]. Acta Armamentarii, 2020, 41(3): 585-594. (in Chinese) doi: 10.3969/j.issn.1000-1093.2020.03.020
[23]	盖江涛, 刘春生, 马长军, 等. 考虑履带滑转滑移的电驱动履带车辆转向控制[J]. 兵工学报, 2021, 42(10): 2092-2101. doi: 10.3969/j.issn.1000-1093.2021.10.005
	GAI J T, LIU C S, MA C J, et al. Steering control of electric drive tracked vehicle considering tracks’ skid and slip[J]. Acta Armamentarii, 2021, 42(10): 2092-2101. (in Chinese)
[24]	吴海东, 司振立. 基于线性矩阵不等式的智能车轨迹跟踪控制[J]. 浙江大学学报(工学版), 2020, 54(1):110-117.
	WU H D, SI Z L. Intelligent vehicle trajectory tracking control based on linear matrix inequality[J]. Journal of Zhejiang University (Engineering Science), 2020, 54(1): 110-117. (in Chinese)
[25]	GHARIB A, STENGER D, RITSCHEL R, et al. Multi-objective optimization of a path-following MPC for vehicle guidance: a Bayesian optimization approach[C]// Proceedings of 2021 European Control Conference. Delft, Netherlands:IEEE, 2021: 2197-2204.

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

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

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

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

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