Tractor sideslip angle real-time estimation system and method based on auxiliary navigation

By fusing dynamic and kinematic models and combining sensor data for real-time estimation of sideslip angle, the problems of insufficient estimation accuracy and response lag in agricultural machinery navigation systems are solved, improving the robustness and accuracy of the navigation system and meeting the needs of high-precision field operations.

CN121799418APending Publication Date: 2026-04-07HAINAN UNIV
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Patent Information

Application Number
CN202610269659.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-06
Publication Date
2026-04-07

AI Technical Summary

Technical Problem

Existing automatic navigation systems for agricultural machinery suffer from insufficient accuracy in sideslip angle estimation, lag in dynamic response, and poor adaptability to unstructured field environments, making it difficult to meet the real-time status feedback requirements of high-precision navigation.

Method used

By constructing a multi-model fusion framework based on dynamic and kinematic models, combining sensor data for state observation, and using the extended Kalman filter algorithm and Luneburg state observer for real-time estimation of tire sideslip angle, the final sideslip angle estimate is obtained by the fusion weighting module.

Benefits of technology

It significantly improves the robustness of the navigation system in perceiving vehicle sideslip, enhances the accuracy of path tracking control and navigation precision, and ensures the consistency and efficiency of automated field operations.

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Abstract

The invention discloses a tractor sideslip angle real-time estimation system and method based on auxiliary navigation, and belongs to the technical field of agricultural machinery intelligent navigation and motion control. The method comprises the steps of obtaining a vehicle motion state vector; constructing an extended kinematics model and a two-degree-of-freedom dynamical model based on the vehicle motion state vector; obtaining the initial state of the tire sideslip angle based on an extended kinematics model and an extended Kalman filtering algorithm; on the basis of a two-degree-of-freedom dynamic model and a Luenberger state observer, carrying out online identification and correction on the initial state of the tire sideslip angle to obtain corrected estimated values of the front and rear tire sideslip angles; and obtaining a fusion weighting factor of the sideslip angles of the front and rear wheels based on the corrected estimated values of the sideslip angles of the front and rear tires, the vehicle speed and the vehicle steering angle, and obtaining a final estimated value of the sideslip angles of the front and rear wheels based on the fusion weighting factor. According to the method, the perception robustness of the navigation system to the vehicle sideslip state can be remarkably improved.
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