基于可拓相平面稳定域划分的Tube-MPC车辆稳定性控制

doi:10.19562/j.chinasae.qcgc.2024.09.013

Abstract

Abstract:

For the stability control problem of distributed four-wheel-drive electric vehicles under extreme conditions， considering the influence of sensor noise of yaw rate， lateral and longitudinal acceleration， as well as the estimation error of slip angle， a phase plane stability domain division method based on extension theory and an adaptive Tube-based Model Predictive Control algorithm （ATMPC） are proposed to quickly quantify the stability level of the vehicle and ensure the vehicle driving stability while maintaining tracking accuracy. The designed vehicle yaw stability control system utilizes hierarchical design architecture. The upper layer employs the extension theory to associate the vehicle slip angle-yaw rate phase plane with extension control domain and determines the control domain based on the actual vehicle state and calculates the dependent function to realize the decision-making of the control target weights and modes of the lower layer's Tube-MPC. The lower layer utilizes Tube-MPC to track the desired vehicle slip angle and yaw rate， enabling precise decision-making regarding the yaw moment， and adopts the tire loading ratios optimization method for the allocation of the yaw moment. The control strategy is validated by Carsim/Simulink co-simulation. The results show that the proposed control framework and ATMPC strategy can significantly enhance the driving stability of vehicles in extreme conditions and improve robustness in noisy environments， outperforming traditional MPC.

Key words: distributed drive vehicle, stability control, extension phase plane, Tube-MPC

Dongxu Su,Zhiguo Zhao,Kun Zhao,Gang Li,Qin Yu. Tube-MPC Vehicle Stability Control Based on Stability Domain Division in Extension Phase Plane[J].Automotive Engineering, 2024, 46(9): 1654-1667.

Figures/Tables 23

参数	数值
整车质量/kg	1 650
轴距/m	3.05
质心到前轴距离/m	1.4
质心到后轴距离/m	1.65
轮距/m	1.60
整车绕z轴转动惯量/（ $k g ? m 2$ ）	3 234

参数		数值
离散步长T/s		0.01
预测时域 $N p$		10
控制时域 $N c$		7
横摆力偶矩	约束范围 $M z / (N · m)$	［-1 200， 1 200］
横摆力偶矩	增量约束范围 $Δ M z / (N · m)$	［-Inf， Inf］
前轮转角	约束范围 $δ f / r a d$	［-0.52， 0.52］
前轮转角	增量约束范围 $Δ δ f / r a d$	［-0.026， 0.026］

References 21

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控制算法	质心侧偏角误差/rad		横摆角速度误差/（rad·s^-1）		行驶轨迹误差/m
控制算法	RMS	Max	RMS	Max	RMS	Max
无控制	0.068 3	0.188 1	0.199 2	0.782 7	0.378 4	2.022 5
MPC	0.016 3	0.035 7	0.044 2	0.175 2	0.252 5	0.795 9
AMPC	0.007 9	0.020 0	0.022 0	0.069 2	0.200 4	0.619 2

控制算法	质心侧偏角误差/rad		横摆角速度误差/（rad·s^-1）		行驶轨迹误差/m
控制算法	RMS	Max	RMS	Max	RMS	Max
无控制	0.017 8	0.068 9	0.050 7	0.242 1	0.148 9	0.610 9
MPC	0.017 0	0.060 6	0.045 8	0.186 6	0.148 7	0.513 0
AMPC	0.013 2	0.047 3	0.031 6	0.130 6	0.147 9	0.452 3

控制算法	质心侧偏角误差/rad		横摆角速度误差/（rad·s^-1）		行驶轨迹误差/m
控制算法	RMS	Max	RMS	Max	RMS	Max
无控制	0.015 6	0.034 2	0.038 9	0.127 5	0.436 2	0.486 6
MPC	0.012 4	0.025 5	0.027 0	0.101 8	0.201 8	0.239 0
ATMPC	0.008 0	0.016 1	0.022 3	0.065 6	0.180 1	0.059 6

Δk_p模糊控制规则
Δk_p		e（s）
Δk_p		NB	NM	NS	ZE	PS	PM	PB
Δe（s）	NB	PB	PB	PM	PM	PS	NS	NS
	NM	PB	PB	PM	PM	PS	NS	NS
	NS	PB	PB	PM	PS	NS	NM	NM
	Z	PB	PB	PM	Z	NS	NM	NB
	PS	PM	PM	PS	NS	NM	NB	NB
	PM	PS	PS	NS	NM	NM	NB	NB
	PB	PS	PS	NS	NM	NM	NB	NB
Δk_i模糊控制规则
Δk_i		e（s）
Δk_i		NB	NM	NS	ZE	PS	PM	PB
Δe（s）	NB	NB	NB	NM	NM	NM	Z	Z
	NM	NB	NM	NM	NM	NS	Z	Z
	NS	NM	NM	NS	NS	Z	PS	PS
	Z	NM	NS	NS	Z	PS	PS	PM
	PS	NS	NS	Z	PS	PS	PM	PM
	PM	Z	Z	PS	PM	PM	PB	PB
	PB	Z	Z	PS	PM	PB	PB	PB
Δk_d模糊控制规则
Δk_d		e（s）
Δk_d		NB	NM	NS	ZE	PS	PM	PB
Δe（s）	NB	PS	PS	Z	Z	Z	PS	PB
	NM	NB	NB	NM	NS	PM	PB	PM
	NS	NB	NB	NM	NS	PS	PS	PM
	Z	NS	NS	NS	NS	Z	PS	PB
	PS	NB	NB	NM	NS	PS	PB	PB
	PM	NB	NB	NM	NS	PM	PB	PB
	PB	PS	PS	Z	Z	Z	PS	PS

控制算法	质心侧偏角误差/rad		横摆角速度误差/（rad·s^-1）		行驶轨迹误差/m
控制算法	RMS	Max	RMS	Max	RMS	Max
MPC	0.016 1	0.044 3	0.049 5	0.229 3	0.215 9	0.905 5
模糊PID	0.009 6	0.028 8	0.029 6	0.140 9	0.203 1	0.782 8
ATMPC	0.005 9	0.019 5	0.025 9	0.072 3	0.196 1	0.489 6

Tube-MPC Vehicle Stability Control Based on Stability Domain Division in Extension Phase Plane

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Figures/Tables 23

References 21

Related Articles 9

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控制算法	质心侧偏角误差/rad		横摆角速度误差/（rad·s^-1）		行驶轨迹误差/m
控制算法	RMS	Max	RMS	Max	RMS	Max
MPC	0.012 9	0.040 2	0.037 2	0.163 9	0.239 8	0.391 5
模糊PID	0.011 0	0.026 2	0.028 3	0.107 4	0.351 1	0.419 9
ATMPC	0.007 6	0.017 5	0.023 5	0.072 7	0.182 0	0.073 0

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