Path-Tracking Control for Four-Wheel Steer/Drive Agricultural Special Electric Vehicles Considering Stability

Author:

Huang Bin,Yang Nuorong,Ma Liutao,Wei Lexia1

Affiliation:

1. Liuzhou Wuling New Energy Automobile Co., Ltd.

Abstract

<div class="section abstract"><div class="htmlview paragraph">With the modernization of agriculture, the application of unmanned agricultural special vehicles is becoming increasingly widespread, which helps to improve agricultural production efficiency and reduce labor. Vehicle path-tracking control is an important link in achieving intelligent driving of vehicles. This paper designs a controller that combines path tracking with vehicle lateral stability for four-wheel steer/drive agricultural special electric vehicles. First, based on a simplified three-degrees-of-freedom vehicle dynamics model, a model predictive control (MPC) controller is used to calculate the front and rear axle angles. Then, according to the Ackermann steering principle, the four-wheel independent angles are calculated using the front and rear axle angles to achieve tracking of the target trajectory. For vehicle lateral stability, the sliding mode control (SMC) is used to calculate the required direct yaw moment control (DYC) of the vehicle, and wheel torque distribution is carried out considering the front and rear axle loads and road adhesion coefficient. CarSim and MATLAB/Simulink were chosen to build a joint simulation platform, and simulation experiments were conducted under two working conditions: high adhesion road surface and low adhesion road surface. The simulation results showed that the controller designed in this paper can improve the lateral stability of the vehicle while ensuring good path-tracking accuracy.</div></div>

Publisher

SAE International

Reference21 articles.

1. Li , Y. , Zhang , B. , Chen , X. , Hang , P. Research on Path Tracking Control of Four Wheel Independent Steering Electric Vehicles Based on Carsim/Simulink Joint Simulation 2020 39th Chinese Control Conference (CCC) Wuhan, China 2020 7

2. Li , X. 2023

3. Liu , Y. , Shi , G. , Chen , Y. et al. Fuzzy Path Tracking Control Method Based on Pure Tracking Model Mechanical Design and Research 38 3 2022 136 140+157 https://doi.org/10.13952/j.cnki.jofmdr.2022.0162

4. Chang , T. , and Shahrampour , S. Distributed Online Linear Quadratic Control for Linear Time-Invariant Systems 2021 American Control Conference (ACC) New Orleans, LA 2021 923 928 https://doi.org/10.23919/ACC50511.2021.9483391

5. Alsterda , J. , Brown , M. , and Gerdes , J. Contingency Model Predictive Control for Automated Vehicles 2019 American Control Conference (ACC) Philadelphia, PA 2019 717 722 https://doi.org/10.23919/ACC.2019.8815260

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