Experimental study on driver seat vibration characteristics of crawler-type combine harvester

One of the most important means of improving the mechanization of rapeseed harvests and increasing farmers’ income is to reduce the cleaning loss of rapeseed. In this study, a fuzzy grey control system was developed using an assembled cleaning loss sensor. Based on experimental data, the relationship between the cleaning loss and the opening of the louver sieve in the cleaning device was obtained. The fuzzy control scheme was established by combining grey prediction and the fuzzy control principle. Secondly, a microcontroller unit (MCU) was used as the controller, and the opening of the louver sieve was automatically regulated by detecting the signal of the cleaning loss. Finally, the performance and robustness of the control system was evaluated in field tests. Different experiments were conducted under different speed conditions to reflect the variable throughput. Results showed that using the grey prediction control system can realize the adjustment of the louver sieve opening in real time. The cleaning loss could be maintained within the ideal setpoint interval, compared with the operation with the control system switched off. These findings indicate that the application of the grey fuzzy control system reduces cleaning loss, and the nonlinear, time-variable and time delay problems in cleaning devices can be solved effectively.

Crop density estimation ahead of the combine harvester provides a valuable reference for operators to keep the feeding amount stable in agriculture production, and, as a consequence, guaranteeing the working stability and improving the operation efficiency. For the current method depending on LiDAR, it is difficult to extract individual plants for mature rice plants with luxuriant branches and leaves, as well as bent and intersected panicles. Therefore, this paper proposes a clustering adaptive density estimation method based on the constructed LiDAR measurement system and double-threshold segmentation. The Otsu algorithm is adopted to construct a double-threshold according to elevation and inflection intensity in different parts of the rice plant, after reducing noise through the statistical outlier removal (SOR) algorithm. For adaptively parameter adjustment of supervoxel clustering and mean-shift clustering during density estimation, the calculation relationship between influencing factors (including seed-point size and kernel-bandwidth size) and number of points are, respectively, deduced by analysis. The experiment result of density estimation proved the two clustering methods effective, with a Root Mean Square Error (RMSE) of 9.968 and 5.877, and a Mean Absolute Percent Error (MAPE) of 5.67% and 3.37%, and the average accuracy was more than 90% and 95%, respectively. This estimation method is of positive significance for crop density measurement and could lay the foundation for intelligent harvest.

In response to the issues faced by conventional combine harvesters' threshing drums, such as the difficulty in maintaining stable speed due to load fluctuations and torque disturbances, as well as the limited speed adjustment range to adapt to changes in crop varieties and properties, this study proposes a novel dual power source threshing drum (DPSTD). By incorporating a power coupling device and a motor into the existing mechanical structure, the DPSTD utilizes the flexible speed and torque output capabilities of the motor to achieve a wide speed adjustment range and enhance its resistance to interference. A robust model predictive control strategy is designed based on finite time disturbance observer (FTDO) to compensate for torque under load fluctuations. Simulation results demonstrate that the DPSTD can achieve rapid and accurate speed regulation. Moreover, under torque disturbances and load mutations, the DPSTD with the torque compensation control strategy, can reduce the speed error by 73.90%. This significantly improves the speed tracking performance of the threshing drum, exhibiting good anti-interference capabilities. Finally, the superiority of the DPSTD is validated through bench tests, confirming its effectiveness and feasibility and providing new insights for control research on threshing drums.