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Research Papers

Coordinated Control of Autonomous Four Wheel Drive Electric Vehicles for Platooning and Trajectory Tracking Using a Hierarchical Architecture

[+] Author and Article Information
Jinghua Guo

State Key Laboratory
of Automotive Safety and Energy,
Tsinghua University,
Beijing 100084, China
e-mail: guojing_0701@live.cn

Keqiang Li

State Key Laboratory
of Automotive Safety and Energy,
Tsinghua University,
Beijing 100084, China
e-mail: likq@tsinghua.edu.cn

Yugong Luo

State Key Laboratory
of Automotive Safety and Energy,
Tsinghua University,
Beijing 100084, China
e-mail: lyg@tsinghua.edu.cn

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 26, 2014; final manuscript received May 6, 2015; published online July 1, 2015. Assoc. Editor: Junmin Wang.

J. Dyn. Sys., Meas., Control 137(10), 101001 (Oct 01, 2015) (18 pages) Paper No: DS-14-1140; doi: 10.1115/1.4030720 History: Received March 26, 2014; Revised May 06, 2015; Online July 01, 2015

This paper presents a systematic method on how to design the coordinated lateral and longitudinal motion control system of autonomous four wheel drive (4WD) electric vehicles for platooning and trajectory tracking. First, mathematical models that perfectly describe the behaviors of autonomous 4WD vehicles are built-up, and the coupled effects in vehicle dynamic systems are given. Second, owing to the fact that autonomous vehicles are large-scale systems with strong coupling, nonlinearities, and uncertainties, a novel multi-objective hierarchical architecture used for coordinated lateral and longitudinal motion control is constructed, which is composed of a global cooperative control layer, a control allocation layer, and an action execution layer. A robust backstepping sliding mode controller (RBSMC) is presented in the cooperative control layer to provide the resultant forces/moment. The control allocation layer is designed using interior-point (IP) algorithm to determine the tire lateral and longitudinal forces, which result in the desired resultant forces/moment. The action execution layer consists of an inverse tire model, a slip ratio regulator for each wheel, and a slip angle regulator. Finally, simulation experiments are carried out under adverse driving conditions, and the results show that the proposed control architecture not only possesses excellent tracking performance but also enhances the riding comfort, stability, and safety of autonomous 4WD electric vehicles.

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References

Figures

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Fig. 1

Schematic of vehicle

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Fig. 2

Relative location of vehicle-to-road

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Fig. 3

Relative location of vehicle-to-vehicle

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Fig. 10

Multi-objective hierarchical architecture for coordinated control

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Fig. 9

Response results with various front-wheel steering angle: (a) lateral velocity, (b) longitudinal velocity, and (c) yaw rate

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Fig. 8

Response results with various torques: (a) lateral velocity, (b) longitudinal velocity, and (c) yaw rate

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Fig. 7

Coupled relationship among lateral, longitudinal, and yaw motions

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Fig. 6

The response of tire forces with various normal forces: (a) lateral force and (b) longitudinal force

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Fig. 5

Tire coupling under saturated constraint

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Fig. 4

Tire forces coupling under inequality constraint: (a) at slip ratio = 0.05, 0.1, 0.15, and 0.2 and (b) at slip angle = 2 deg, 4 deg, 6 deg, and 8 deg

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Fig. 11

Equivalent inequality constraints

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Fig. 16

Comparisons of the desired and actual tire slip ratios: (a) FL slip ratio, (b) FR slip ratio, (c) RL slip ratio, and (d) RR slip ratio

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Fig. 17

Comparisons of the desired and actual tire slip angles: (a) FL slip ratio and (b) FR slip ratio

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Fig. 18

Desired preceding velocity and acceleration in braking case: (a) desired preceding velocity and (b) curvature

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Fig. 15

Desired tire forces provided by the control allocation layer: (a) Front left tire (FL) force, (b) Front right tire (FR) force, (c) Rear left tire (RL) force, and (d) Rear right tire (RR) force

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Fig. 19

Simulation results: (a) lateral error, (b) angular error, (c) longitudinal error, and (d) yaw rate

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Fig. 20

Simulation results: (a) lateral error, (b) angular error, (c) longitudinal error, and (d) yaw rate

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Fig. 21

Desired preceding velocity and acceleration in uncertainties: (a) desired preceding velocity and (b) curvature

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Fig. 12

The profile of reference path

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Fig. 13

Desired preceding velocity and acceleration in driving case: (a) desired preceding velocity and (b) desired preceding acceleration

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Fig. 14

Simulation results: (a) lateral error, (b) angular error, (c) longitudinal error, (d) lateral velocity, (e) sideslip angle, and (f) yaw rate

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Fig. 22

Simulation results: (a) lateral error, (b) angular error, (c) longitudinal error, (d) lateral velocity, (e) sideslip angle, and (f) yaw rate

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Fig. 23

Desired tire forces provided by the control allocation layer: (a) FL force, (b) FR force, (c) RL force, and (d) RR force

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