Research on finite-time trajectory tracking control of autonomous agricultural vehicles in complex farmland environments

Aiming at the problem that the tracking accuracy of autonomous agricultural vehicles (AAVs) is reduced in the complex farmland environment. A fixed-time second-order terminal sliding mode control (TSMC) is proposed to obtain an accurate upper bound of settling time. Then, the cornering stiffness estimation method is employed to address the challenges caused by parameter uncertainties, and a nonlinear disturbance observer (NDO) is utilized to estimate and compensate for external disturbances. To further improve tracking accuracy, a sub-regional dynamic gain adjustment mechanism is proposed to compensate for the error caused by the disturbance observer and adaptation, thereby improving the tracking accuracy of AAVs. Finally, Carsim/Simulink is utilized to validate the effectiveness and stability of the proposed control strategy.

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Publication Details

Journal
Transactions of the Institute of Measurement and Control
Published
2025-12-04
DOI
https://doi.org/10.1177/01423312251392321
Primary Topic
Control and Dynamics of Mobile Robots
Type
article
Field-Weighted Citation Impact
0.00
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article

Research on finite-time trajectory tracking control of autonomous agricultural vehicles in complex farmland environments

Huizi Chen, Xinyong Li, Guangzhen Cui, Yanqiu Xiao et al.
Transactions of the Institute of Measurement and Control
Control and Dynamics of Mobile Robots
article

Research on finite-time trajectory tracking control of autonomous agricultural vehicles in complex farmland environments

Huizi Chen, Xinyong Li, Guangzhen Cui, Yanqiu Xiao, Lei Yao
article en

Abstract

Aiming at the problem that the tracking accuracy of autonomous agricultural vehicles (AAVs) is reduced in the complex farmland environment. A fixed-time second-order terminal sliding mode control (TSMC) is proposed to obtain an accurate upper bound of settling time. Then, the cornering stiffness estimation method is employed to address the challenges caused by parameter uncertainties, and a nonlinear disturbance observer (NDO) is utilized to estimate and compensate for external disturbances. To further improve tracking accuracy, a sub-regional dynamic gain adjustment mechanism is proposed to compensate for the error caused by the disturbance observer and adaptation, thereby improving the tracking accuracy of AAVs. Finally, Carsim/Simulink is utilized to validate the effectiveness and stability of the proposed control strategy.

Transactions of the Institute of Measurement and Control
Zhengzhou University of Light Industry (CN)
Openalex Percentile: Top 52%
Control and Dynamics of Mobile Robots
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