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Tractor Path Tracking Control Method Based on Prescribed Performance and Sliding Mode Control

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Publicado en:Agriculture vol. 15, no. 15 (2025), p. 1663-1679
Autor principal: Zhu, Liwei
Otros Autores: Sun, Weiming, Zhang, Qian, Lu, En, Xue Jialin, Sha Guohui
Publicado:
MDPI AG
Materias:
Kinematics
Velocity
Agricultural equipment
Accuracy
Agricultural land
Field tests
Control algorithms
Agricultural production
Tracking control
Agricultural technology
Tractors
Path tracking
Design
Sliding mode control
Methods
Error reduction
Control stability
Control methods
Agricultural vehicles
Tracking errors
Constraints
Farm machinery
Agricultural development
Environmental
Acceso en línea:Citation/Abstract
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Resumen:In addressing the challenges of low path tracking accuracy and poor robustness during tractor autonomous operation, this paper proposes a path tracking control method for tractors that integrates prescribed performance with sliding mode control (SMC). A key feature of this control method is its inherent immunity to system parameter perturbations and external disturbances, while ensuring path tracking errors are constrained within a predefined range. First, the tractor is simplified into a two-wheeled vehicle model, and a path tracking error model is established based on the reference operation trajectory. By defining a prescribed performance function, the constrained tracking control problem is transformed into an unconstrained stability control problem, guaranteeing the boundedness of tracking errors. Then, by incorporating SMC theory, a prescribed performance sliding mode path tracking controller is designed to achieve robust path tracking and error constraint for the tractor. Finally, both simulation and field experiments are conducted to validate the method. The results demonstrate that compared with the traditional SMC method, the proposed method effectively mitigates the impact of complex farmland conditions, reducing path tracking errors while enforcing strict error constraints. Field experiment data shows the proposed method achieves an average absolute error of 0.02435 m and a standard deviation of 0.02795 m, confirming its effectiveness and superiority. This research lays a foundation for the intelligent development of agricultural machinery.
ISSN:2077-0472
DOI:10.3390/agriculture15151663
Fuente:Agriculture Science Database