Authors

Abstract

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.

Abstract in Chinese

犁耕工况下拖拉机驱动轮滑转率的在线准确测量对提升拖拉机作业性能和犁耕质量具有重要意义。为解决当前犁耕工况下拖拉机滑转率滑转率测量精度低、抗干扰能力差、测量效率低等问题，本文提出一种基于多传感器融合与自适应滤波的拖拉机滑转率在线测量方法。首先，本文利用GNSS、IMU与轮速编码器等多源传感器搭建了拖拉机滑转率在线测量系统。然后，引入基于透镜成像的准对立学习策略与优点集初始化机制，构建了基于改进蜉蝣算法优化的并行卡尔曼滤波算法（IMA-PKF），实现了犁耕作业随机噪声扰动的自适应实时调整，增强了算法对犁耕工况的鲁棒性。仿真结果表明，无干扰条件下，IMA-PKF算法下滑转率的估计值RMSE为0.0214，较传统KF降低74.8%，优于PSO-PKF和MA-PKF，分别提升62.23%、49.41%；引入扰动点后， RMSE仅为0.0359，具有良好的稳定性与抗干扰能力。田间试验结果进一步验证了该方法在不同耕深工况下的鲁棒性，滑转率测量值最大误差仅1.94%，偏差控制2%以内，较KF算法下的MAE和RMSE分别降低36.29%与37.06%。综上，IMA-PKF算法实现了不同犁耕工况下拖拉机驱动轮滑转率的在线准确测量，为提升拖拉机整机性能和作业效率奠定了理论基础和技术支撑。