ONLINE MEASUREMENT METHOD FOR TRACTOR DRIVE WHEEL SLIP RATIO BASED ON IMA-PKF

Résumé fourni par la source

Accurate and real-time measurement of tractor drive wheel slip ratio under plowing conditions is essential for improving overall machine performance and tillage quality. To address the limitations of existing methods—namely low measurement accuracy, poor anti-interference capability, and low efficiency—this study proposes an online slip ratio measurement method based on multi-sensor fusion and adaptive filtering. A real-time measurement system was developed by integrating GNSS, IMU, and wheel encoders. Furthermore, a lens-based quasi-oppositional learning strategy and a good-point-set initialization mechanism were introduced to enhance the mayfly algorithm, which was then used to optimize a parallel Kalman filter, forming the improved mayfly algorithm–parallel Kalman filter (IMA-PKF). This approach enables adaptive real-time adjustment to random noise disturbances encountered during plowing operations, thereby enhancing robustness. Simulation results show that under non-interference conditions, the IMA-PKF algorithm achieves a root mean squared error (RMSE) of 0.0214, representing a 74.8% reduction compared with the conventional KF algorithm. In addition, compared with PSO-PKF and MA-PKF, the RMSE accuracy is improved by approximately 62.23% and 49.41%, respectively. When disturbance points are introduced, IMA-PKF still maintains the lowest estimation error, with an RMSE of 0.0359, demonstrating excellent stability and anti-interference capability. Field experiments under different plowing depths further validate the robustness of the method: the maximum slip ratio measurement error is only 1.94%, with bias controlled within 2%. Compared with KF, the proposed method reduces mean absolute error (MAE) and RMSE by up to 36.29% and 37.06%, respectively. Overall, the IMA-PKF algorithm enables accurate and stable online measurement of tractor drive wheel slip ratio under diverse plowing conditions, providing a solid theoretical and technical foundation for improving tractor performance and operational efficiency.

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