Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control
For four-wheel independent drive intelligent vehicle, the longitudinal and lateral motion control of the vehicle is decoupled and a hierarchical controller is designed: the upper layer is the motion controller, and the lower layer is the control distributor. In the motion controller, the model predi...
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2021-09-01
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/16878140211045142 |
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doaj-45cf523a0f054817bd5cdef9314c04852021-09-30T00:03:23ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402021-09-011310.1177/16878140211045142Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode controlHaiDong WuZiHan LiZhenLi SiFor four-wheel independent drive intelligent vehicle, the longitudinal and lateral motion control of the vehicle is decoupled and a hierarchical controller is designed: the upper layer is the motion controller, and the lower layer is the control distributor. In the motion controller, the model predictive control (MPC) is used to calculate the steering wheel angle and the total yaw moment for lateral control, and the sliding mode control (SMC) is used to calculate the total driving force for longitudinal control. In order to improve the control algorithm adaptability and the tracking accuracy at high speed, the UniTire model that can accurately express the complex coupling characteristics of tire under different working conditions are used and the numerical partial derivative of the state equation is used in MPC controller to ensure the feasibility of the algorithm. The control distributor distributes the total yaw moment and driving force calculated by the motion controller of the four wheels through the objective optimization function, and the constraints on road adhesion condition and the constraints on actuators are considered at the same time. A co-simulation platform is built in the CarSim/Simulink environment and the MPC-SMC controller is compared with the previously established MPC controller.https://doi.org/10.1177/16878140211045142 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
HaiDong Wu ZiHan Li ZhenLi Si |
spellingShingle |
HaiDong Wu ZiHan Li ZhenLi Si Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control Advances in Mechanical Engineering |
author_facet |
HaiDong Wu ZiHan Li ZhenLi Si |
author_sort |
HaiDong Wu |
title |
Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control |
title_short |
Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control |
title_full |
Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control |
title_fullStr |
Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control |
title_full_unstemmed |
Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control |
title_sort |
trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2021-09-01 |
description |
For four-wheel independent drive intelligent vehicle, the longitudinal and lateral motion control of the vehicle is decoupled and a hierarchical controller is designed: the upper layer is the motion controller, and the lower layer is the control distributor. In the motion controller, the model predictive control (MPC) is used to calculate the steering wheel angle and the total yaw moment for lateral control, and the sliding mode control (SMC) is used to calculate the total driving force for longitudinal control. In order to improve the control algorithm adaptability and the tracking accuracy at high speed, the UniTire model that can accurately express the complex coupling characteristics of tire under different working conditions are used and the numerical partial derivative of the state equation is used in MPC controller to ensure the feasibility of the algorithm. The control distributor distributes the total yaw moment and driving force calculated by the motion controller of the four wheels through the objective optimization function, and the constraints on road adhesion condition and the constraints on actuators are considered at the same time. A co-simulation platform is built in the CarSim/Simulink environment and the MPC-SMC controller is compared with the previously established MPC controller. |
url |
https://doi.org/10.1177/16878140211045142 |
work_keys_str_mv |
AT haidongwu trajectorytrackingcontrolforfourwheelindependentdriveintelligentvehiclebasedonmodelpredictivecontrolandslidingmodecontrol AT zihanli trajectorytrackingcontrolforfourwheelindependentdriveintelligentvehiclebasedonmodelpredictivecontrolandslidingmodecontrol AT zhenlisi trajectorytrackingcontrolforfourwheelindependentdriveintelligentvehiclebasedonmodelpredictivecontrolandslidingmodecontrol |
_version_ |
1716864009497477120 |