玉米低损籽粒直收机自动控制系统设计与试验

针对玉米籽粒直收机收获过程中无法自主调整工作参数，导致极端作业条件下收获后籽粒破碎率偏高的问题，该研究以降低籽粒破碎率为目标，设计了一种玉米籽粒直收低损收获自动控制系统。以4LZ-8型玉米籽粒直收机为研究对象，建立了脱粒滚筒转速、凹板间隙和行车速度控制模型，并基于收获参数对籽粒破碎率的回归模型，设计了低损收获自动控制策略。此外，针对传统PID控制系统存在的响应时滞、超调量大、精度差的问题，设计了基于改进粒子群算法的自动控制系统，利用非线性惯性权重递减算法融合布谷鸟算法的随机游走策略，不断更新粒子群的速度和位置，并对改进粒子群算法进行了性能测试，结果表明该算法有效改善了标准粒子群算法容易陷入局部最优值的问题。对低损收获自动控制系统进行的仿真对比试验和田间验证试验结果表明，改进粒子群算法对脱粒滚筒转速、凹板间隙和行车速度具有较好的控制精度、响应速度和稳定性，超调量和超调时间较小，当脱粒滚筒转速为380 r/min、凹板间隙为42 mm、行车速度为2.5 km/h时，自动控制系统在3 s以内调整籽粒直收机作业参数，将籽粒破碎率最终稳定在3.80%左右，满足标准要求。研究成果可为其他作物生产机...

Design and experiment of the automatic control system for low damage corn grain direct harvesters

Abstract: A direct harvester is seriously limited for the high broken kernel rate with the high moisture during harvesting. The harvest quality can significantly dominate the yield and quality. Among them, corn threshing is one of the most essential links in the corn harvesting process. The corn ears can usually be harvested, when the moisture content of the kernel is in the range of 20%-40%. Then, the corn kernel is threshed after the moisture content reduced to about 15% after drying. However, the traditional treatment cannot meet the high requirements of modern corn production, due to the long working period, high labor intensity, and high operating costs. Furthermore, it is necessary to manually adjust the operating parameters of the harvesters when observing the harvest situation, particularly under the very complicated and harsh harvesting environment in the actual production. An automatic control system is still lacking on the harvesting operating parameters for the higher productivity of agricultural machinery and equipment. In addition, the blockage in the threshing device can result in the high broken kernel rate under the uneven growth density of corn plants and the different planting agronomy, as the feeding amount of corn ears increases sharply during harvesting. Therefore, it is a high demand to timely regulate the harvesting parameters for the better operational performance of agricultural machinery and equipment. The automatic control of corn grain harvester is of great significance for the smart agriculture and digital agriculture. Fortunately, the direct harvest mode of corn kernel can be used to improve the operation efficiency with the less harvest time. This study aims to design a set of automatic control solutions to the low damage corn kernel threshing. An automatic control system was proposed to reduce the high broken kernel rate and sluggish system response of corn kernel direct harvester for the high control precision using an improved particle swarm optimization-cuckoo algorithm. Firstly, the mathematical models were established for the threshing cylinder speed-regulating motor, concave clearance regulating electric push rod motor and driving speed regulating motor, as well as the harvesting model of corn ear. Then, the automatic control logic of low damage threshing was also established, according to the influence of corn kernel harvesting parameters on the broken kernel rate. The nonlinear decreasing algorithm was used to change the particle number and inertia weight. The random walk strategy of the Cuckoo algorithm was introduced into the particle swarm optimization. The speed and position of the particle swarm were constantly updated to effectively prevent the particle swarm optimization from falling into the optimal local solution. Simulink simulation was implemented to compare the control effects of Fuzzy PID, PSO-PID, and PSO-CS Fuzzy PID algorithms on the threshing cylinder rotational speed, concave clearance, car speed, and broken kernel rate. The results showed that the improved PSO algorithm performed the best in the control accuracy, response speed, and stability. The field test of the corn kernel direct harvester was carried out to verify the improved model. The broken kernel rate was counted with the automatic control system opening and closing. The automatic control system was effectively improved the operational performance of the harvester, while the kernel broken rate was stable at about 3.8%, indicating the higher stability and accuracy of automatic control system. The findings can also provide a strong reference for the automation development of crop production machinery.