无人车辆底盘集成动力学系统解耦控制研究

doi:10.19562/j.chinasae.qcgc.2021.11.013

摘要/Abstract

摘要：

面向多轮分布式驱动全轮转向无人车辆，本文针对性提出并验证了一种底盘动力学集成控制系统的解耦控制器，旨在通过解耦控制来实现灵活、快速、精确的无人平台动力学性能进一步提升。首先建立了可准确反映车辆纵向-侧向-横摆-侧倾运动的车辆耦合动力学模型，结合非参数统计方法对该动力学系统的输入输出耦合特性进行了定量分析；随后，基于神经网络逆系统原理构建了解耦线性化的车辆动力学系统与解耦复合控制器，测试并成功验证了系统的解耦响应，并对所提出的控制器进行了试验验证。结果表明，耦合动力学系统中各自由度控制子通道间因动力学耦合关系带来的干扰和影响得到了有效削弱，从而可实现对各子通道相对独立的控制效果，为分布式驱动无人车辆在轨迹跟踪运动过程中的高效、精准、稳定的综合动力学表现奠定了重要的平台技术和物理基础。

关键词: 分布式独立驱动, 轮式无人车辆, 底盘集成控制, 神经网络逆解耦控制

Abstract:

This paper proposes and verifies a decoupling controller of chassis dynamics integrated control system for the multi wheel distributed drive all-wheel steering unmanned vehicle， in order to further improve the dynamic performance of flexible， fast and accurate unmanned platform through decoupling control. Firstly， a vehicle coupling dynamic model is established which can accurately reflect the longitudinal， lateral and yaw motion of the vehicle， and the input-output coupling characteristics of the dynamic system are quantitatively analyzed combined with the nonparametric statistical method. Then， based on the principle of Neural Network Inverse （NNI） system， the vehicle dynamic system with decoupling linearization and decoupling composite controller are constructed. The decoupling response of the system is tested and verified successfully， and the proposed controller is verified by experiments. The results show that the interference caused by the dynamic coupling relationship between the control sub-channels of each degree of freedom of the coupling dynamic system is effectively reduced， so that relatively independent control of each sub-channel can be realized， which lays an important platform and physical foundation for efficient， accurate and stable comprehensive dynamic performance in the trajectory tracking process of distributed drive unmanned vehicle.

Key words: distributed independent drive, wheeled unmanned vehicle, chassis integrated control, neural network inverse decoupling control

张雨甜,李斐然,田汉青,胡纪滨,魏超,吴维. 无人车辆底盘集成动力学系统解耦控制研究[J]. 汽车工程, 2021, 43(11): 1673-1682.

Yutian Zhang,Feiran Li,Hanqing Tian,Jibin Hu,Chao Wei,Wei Wu. Research on Decoupling Control of Integrated Dynamics System of Unmanned Vehicle Chassis[J]. Automotive Engineering, 2021, 43(11): 1673-1682.

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参考文献 19

1	陈慧岩，张玉. 军用地面无人机动平台技术发展综述［J］. 兵工学报， 2014， 35（10）： 1696-1706.
	CHEN Huiyan， ZHANG Yu. An overview of research on military unmanned ground vehicles［J］. ACTA Armamentarii， 2014， 35（10）： 1696-1706.
2	DING J， GUO K. Development of a generalised equivalent estimation approach for multi-axle vehicle handling dynamics［J］. Vehicle System Dynamics， 2016， 54（1）：20-57.
3	GUO J， LUO Y， LI K. An adaptive hierarchical trajectory following control approach of autonomous four-wheel independent drive electric vehicles［J］. IEEE Transactions on Intelligent Transportation Systems， 2018， 19（8）：2482-2492.
4	张利鹏，李亮，祁炳楠. 轮毂电机驱动电动汽车侧倾稳定性解耦控制［J］. 机械工程学报， 2017， 53（16）： 94-104.
	ZHANG Lipeng， LI Liang， QI Bingnan. Decoupled roll stability control of in-wheel mmotor drive electric vehicle［J］. Journal of Mechanical Engineering， 2017， 53（16）： 94-104.
5	KATSUYAMA E. Decoupled 3D moment control using in-wheel motors［J］. Vehicle System Dynamics， 2013， 51（1）： 18-31.
6	LI M， JIA Y. Decoupling control in velocity-varying four-wheel steering vehicles with H_∞ performance by longitudinal velocity and yaw rate feedback［J］. Vehicle System Dynamics， 2014， 52（12）.
7	李春文，苗原，冯元琨，等．非线性系统控制的逆系统方法(Ⅱ)-多变量控制理论[J].控制与决策,1997,12(6)：625-630.
	LI Chunwen， MIAO Yuan， FENG Yuankun. Inverse system method for nonlinear systems control（Ⅱ）- multivariable control theory［J］. Control and Decision， 1997，12（6）：625-630.
8	戴先中，刘军，冯纯伯. 连续非线性系统的神经网络α阶逆系统控制方法［J］. 自动化学报， 1998， 24（4）： 463-468.
	DAI Xianzhong， LIU Jun， FENG Chunbo. Neural network αth-order inverse-system control method for nonlinear continuous systems［J］. ACTA Automatica Sinica， 1998， 24（4）： 463-468.
9	朱茂飞，陈无畏，夏光. 基于神经网络逆系统方法的汽车底盘解耦控制［J］. 农业机械学报， 2011，12（5）：13-17.
	ZHU Maofei， CHEN Wuwei， XIA Guang. Vehicle chassis decoupling control based on neural network inverse method［J］. Transactions of the Chinese Society for Agricultural Machinery， 2011，12（5）：13-17.
10	ZHANG H， ZHAO W. Decoupling control of steering and driving system for in-wheel-motor-drive electric vehicle［J］. Mechanical Systems & Signal Processing， 2018， 101：389-404.
11	WANG C， ZHAO W， LUAN Z， et al. Decoupling control of vehicle chassis system based on neural network inverse system［J］. Mechanical Systems and Signal Processing， 2018， 106：176-197.
12	WU H， GAO Q， WANG C， et al. Decoupling control of chassis integrated system for electric wheel vehicle［J］. Proceedings of the Institution of Mechanical Engineers， Part D： Journal of Automobile Engineering， 2020， 234 （6）：1515-1531.
13	YU Y， LI Y， LIANG Y， et al. Dynamic decoupling and trajectory tracking for automated vehicles based on the inverse system［J］. Applied Sciences， 2020， 10（21）：7394.
14	SHI K， YUAN X， et al. Compensation-based robust decoupling control system for the lateral and longitudinal stability of distributed drive electric vehicle ［J］. IEEE/ASME Transactions on Mechatronics， 2019， 24（6）：2768-2778.
15	CORDER G W， FOREMAN D I. Nonparametric statistics for non-statisticians［M］. Wiley & Sons， 2009.
16	陈思忠，孟祥，杨林. 三轴汽车多轮转向技术研究［J］. 北京理工大学学报， 2005， 25（8）：679-683.
	CHEN Sizhong， MENG Xiang， YANG Lin. Multi-wheel steering characteristics of a three-axial vehicle［J］. Transacations of Beijing Institute of Technology， 2005， 25（8）：679-683.
17	WANG J， ZHENG L， YAN W， et al. Coordination control of differential drive assist steering and vehicle stability control for four-wheel-independent-drive EV［J］. IEEE Transactions on Vehicular Technology， 2018， 67（12）：11453-11467.
18	HUANG G, ZHU Q, SIEW C. Extreme learning machine: theory and applications［J］. Neurocomputing, 2006, 70(1-3): 489-501.
19	龚建伟，刘凯，齐建勇. 无人驾驶车辆模型预测控制［M］. 2版. 北京：北京理工大学出版社， 2020.
	GONG J，LIU K，QI J. Model predictive control for self-driving vehicles［M］. 2nd ed. Beijing： Beijing Institute of Technology Press， 2020.