Integrated Coordination Control for Distributed Drive Electric Vehicle Trajectory Tracking

Li, Hongluo; Luo, Yuanfang

doi:10.1007/s12239-020-0099-3

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…Many different control algorithms have been proposed in the field of trajectory tracking, such as linear quadratic regulator (LQR), PID, sliding mode control, model predictive control (MPC), etc. Li designed a coordinated controller that integrates an MPC trajectory tracking controller and a velocity tracking controller, which is better in terms of tracking accuracy and body stability compared to active front wheel steering (AFS) [4]. Jeong and Yim converted the path tracking problem into the yaw rate tracking one, and avoided the disadvantages of the error dynamics based method [5].…”

Section: Introductionmentioning

confidence: 99%

A study on integrated control of trajectory tracking and lateral stability for distributed drive autonomous vehicle

Shi,

Yin

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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In this paper, an integrated control strategy integrating trajectory tracking and lateral stability is proposed for a distributed drive autonomous vehicle (DDAV). First, a model prediction controller is established based on a 3 degree-of-freedom (DOF) vehicle dynamics model and a path following model, and the optimal front wheel steering angle and external yaw moment are simultaneously obtained. Then the torque distribution controller optimises the distribution of longitudinal forces at each wheel with the tyre load rate minimisation as the objective function, and applies the interior point method to solve the optimal torque distribution problem. Finally, simulation experiments are carried out based on the Carsim-Simulink joint simulation platform. The results show that the proposed integrated control strategy can effectively control the lateral error and heading error in vehicle trajectory tracking, and ensure reliable lateral stability of the vehicle at the same time.

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Section: Introductionmentioning

confidence: 99%

A study on integrated control of trajectory tracking and lateral stability for distributed drive autonomous vehicle

Shi,

Yin

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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“…In the case of electric drive vehicles, the driving, anti-skid mechanism, and tracking are generally realized by a motor control strategy [8], such as the model predictive control [9,10], hierarchical control strategy [11], or electronic stability control algorithm [12]. Sliding mode control (SMC) is a variable structure control method [13] that has been widely used in mechanical and vehicle engineering [14,15], especially for clutch control [16] and motor control [17].…”

Section: Introductionmentioning

confidence: 99%

Travel Reduction Control of Distributed Drive Electric Agricultural Vehicles Based on Multi-Information Fusion

Sun

Zhou

et al. 2022

Agriculture

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In agricultural vehicles with internal combustion engines, owing to the use of rear-wheel drive or four-wheel drive, it is difficult to obtain information regarding the slip of the driving wheels. Excessive wheel slip, an inevitable phenomenon occurring during agricultural activities, can easily damage the original soil surface and result in excessive energy consumption. To solve these problems, a distributed drive agricultural vehicle (DDAV) based on multi-information fusion was proposed. The actual travel reduction of each wheel was calculated by determining the vehicle parameters in order to deliver the required torque to the four drive wheels via sliding mode control (SMC) and incremental proportional-integral (PI) control. Through this process, the vehicle always operates in a straight line. Test results show that, on a uniform surface, the travel reduction of each wheel can be maintained at the target value by using the incremental PI control strategy, with only minor fluctuations, to make the vehicle run in a straight line (R2 = 0.9999). Furthermore, on a separated surface, the travel reduction of each wheel can be maintained at the target value, and using the SMC strategy enables more identical coefficient of gross tractions for each wheel to make the vehicle run in a straight line (R2 = 0.9902). Unlike the non-control strategy, the vehicle reaches a stable state within 1 s, owing to the use of a controller that can effectively reduce the impact of road changes on vehicle velocity. This study can provide a reference for the drive control of DDAVs.

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Model Predictive Trajectory Optimization and Tracking in Highly Constrained Environments

Liu

Qian

et al. 2022

Int.J Automot. Technol.

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Integrated Coordination Control for Distributed Drive Electric Vehicle Trajectory Tracking

Cited by 8 publications

References 27 publications

A study on integrated control of trajectory tracking and lateral stability for distributed drive autonomous vehicle

A study on integrated control of trajectory tracking and lateral stability for distributed drive autonomous vehicle

Travel Reduction Control of Distributed Drive Electric Agricultural Vehicles Based on Multi-Information Fusion

Model Predictive Trajectory Optimization and Tracking in Highly Constrained Environments

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