考虑动力迟滞的分布式驱动车辆平面运动控制

董家臣; 高钦和; 刘志浩

doi:10.13700/j.bh.1001-5965.2022.0887

考虑动力迟滞的分布式驱动车辆平面运动控制

doi: 10.13700/j.bh.1001-5965.2022.0887

火箭军工程大学导弹工程学院，西安 710025

基金项目: 国家自然科学基金(51905541)；陕西省自然科学基础研究计划(2020JQ487)；陕西省高校科协青年人才托举计划(20190412)

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E-mail：gaoqh206@163.com

中图分类号: U461
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出版历程
- 收稿日期: 2022-11-03
- 录用日期: 2022-12-09
- 网络出版日期: 2023-02-06
- 整期出版日期: 2024-12-31

Planar motion control of distributed-driven vehicles considering dynamic hysteresis

Missile Engineering Institute，Rocket Force University of Engineering，Xi’an 710025，China

Funds: National Natural Science Foundation of China (51905541); Natural Science Basic Research Program of Shaanxi (2020JQ487); Young Talent Fund of University Association for Science and Technology in Shaanxi, China (20190412)

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Corresponding author: E-mail：gaoqh206@163.com

摘要

摘要:
针对分布式驱动车辆平面运动横纵耦合特点及电驱动、轮胎响应延迟特性，提出一种考虑动力系统惯性迟滞、驱动与转向协同分配的安全行驶控制策略。将动力迟滞简化为轮胎力一阶惯性环节，与系统方程联立后建立升阶的车辆平面动力学模型；设计了包含参考状态生成层、轨迹跟踪控制层和控制量最优分配层的分层协同控制算法。使用单轨模型将驾驶员操作指令转译为期望稳态速度；基于抗扰模型预测控制器(DRMPC)设计轨迹跟踪算法，引入集总扩张状态观测器(ESO)进行扰动观测与系统重构，使用模型预测控制器(MPC)生成期望广义纵向力、侧向力和横摆力矩；底层基于系统模型执行控制量分配，通过雅可比线性化将驱动力矩与主动转向角解耦，视为等效控制量参与分配，并以最小轮胎力利用率为目标设计面向行驶安全的驱动/转向协同最优分配算法。处理器在环(PIL)实验表明：动力系统惯性迟滞对车辆运动状态存在影响；与忽略迟滞效应的传统控制策略相比，设计的分层控制策略在双移线工况测试中展现出控制精度和响应速度上的优势，在匀加速正弦转向工况测试中，通过控制力的再分配完成了高精度的平面运动控制，轮胎力利用率仅为传统基于规则分配策略的38.2%。
- 分布式驱动车辆 /
- 分层控制 /
- 模型预测控制 /
- 扩张状态观测器 /
- 控制量分配
Abstract:
A hierarchical control strategy is proposed for distributed-driven vehicles, taking into account the powertrain dynamical hysteresis and the coordination of motor torques and wheel steerings. This is based on the coupling characteristics of vehicle planar motion in longitudinal, lateral, and yaw directions, as well as the hysteresis characteristics of electric driving and tire response. The dynamic hysteresis is simplified to the first-order inertial term of tire force, and combined with the planar motion model to establish an ascending-order vehicle dynamics model. Then a hierarchical coordinated control strategy is proposed, containing a reference state generator, a feedback trajectory tracking controller, and an optimal control allocator. To be more precise, the top layer uses a single-track model to convert the driver's commands into expected steady-state motion states. Next, the tracking controller is established using disturbance rejection model predictive control (DRMPC), and a lumped extended state observer (ESO) is used for disturbance observation and feedback compensation. A model predictive controller (MPC) is then introduced to produce generalized longitudinal force, lateral force, and yaw moment. Finally, the bottom layer executes optimal control allocation, decoupling motor torques and wheel angles using Jacobi linearization and treating them as equivalent control inputs in the optimal allocation with the goal of minimizing tire force utilization. Processor in the loop (PIL) experiments show that: dynamic hysteresis generates an obvious effect on vehicle planar motion; the proposed hierarchical control strategy presents advantages in control accuracy and response speed in the double-lane-change maneuver compared with conventional control strategy which ignores the hysteresis; in the constant-acceleration-sinusoidal-steering test, the proposed algorithm realizes planar motion control through control force redistribution, and the tire force loading rate is only 38.2% of the traditional rule-based allocation strategy.
- distributed-driven vehicle /
- hierarchical control /
- model predictive control /
- extended state observer /
- control allocation

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图 1 车辆平面动力学模型示意图

Figure 1. Diagram of vehicle planar dynamics model

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图 2 本文策略框图

Figure 2. Block diagram of the proposed strategy

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图 3 单轨模型示意图

Figure 3. Diagram of a single-track model

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图 4 ESO扰动观测补偿示意图

Figure 4. Disturbance observation and compensation of ESO

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图 5 处理器在环实验平台

Figure 5. Test bench of processor in the loop experiment

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图 6 动力系统迟滞影响

Figure 6. Influence of the drive system hysteresis

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图 7 双移线工况下平面运动控制曲线

Figure 7. Planar motion control curves under double-lane change maneuver

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图 8 匀加速正弦转向工况下的控制曲线

Figure 8. Control curves under constant acceleration & sinusoidal steering maneuver

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表 1 车体参数

Table 1. Vehicle parameters

m/kg	I_z/（kg·m²）	l_f1/m	l_f2/m	l_f3/m	l_f4/m	l_s/m	R_eff/m	C_α	h/m
1460	2500	1.015	1.015	−1.9	−1.9	0.96	0.325	2.1×10⁴	0.54

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表 2 控制参数

Table 2. Controller parameters

参数	数值
ω_o	10
p	4
Q	diag{[2×10², 4×10², 2×10³,1,1,1]}×6×10³
R	diag{[2,1,1]}×10⁻¹
[a_xmax ,a_ymax ,a_zmax]	[5 m/s²,1 m/s²,5 rad/s²]
[V_xmax, V_ymax, Ω_zmax]	[30 m/s,5 m/s,2 rad/s]
k_max	5×10²×[1,1,1,1,0.01,0.01]

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