基于GWO-MPC的联合收获机喂入量控制方法与仿真实验

收获机作为农业生产的重要生产工具，其喂入量控制一直是自动控制领域研究的热点问题。本文通过分析收获机工作方式，建立收获时收获机喂入量变化模型。设计开发收获机作业参数监测系统，以小麦作为实验对象，在我国华北地区开展田间实验，验证系统喂入量监测精度并同步采集产量、含水率和作业速度等参数，系统喂入量监测平均相对误差为8.55%。以收获机在割台高度不变条件下保持额定喂入量为控制目标状态，收获机作业速度作为控制量，采用模型预测的方法对收获机喂入量进行仿真控制。采用灰狼优化算法优化二次规划的权值矩阵，仿真结果表明，权值矩阵优化后，喂入量控制平均绝对误差小于0.1 kg/s,平均降低38.1%。喂入量控制误差与收获区域的产量成反比，与含水率成正比。在相邻时域内产量、含水率变化较小的收获区域效果更好。

Feed Rate Control Method and Simulation Experiment of Combine Harvester Based on GWO-MPC

Combine harvester is an important tool of agricultural production. The feed rate of harvester has always been a hot topic in the area of automatic control. By analyzing the operation mode of harvester, the change model of feed rate of harvester was established. A monitoring system for state parameters of harvester was designed and developed. The system includes grain flow sensor, header height sensor, grain moisture content sensor, feed rate sensor and global navigation satellite system. Taking wheat as the experimental object, a field experiment was carried out in North China to verify the monitoring accuracy of feed rate in different yield moisture content plots. The parameters such as feed rate, yield, moisture content and operation speed were also collected simultaneously. The average relative error of feed rate monitoring was 8.55%. Taking the feed rate of harvester as the control target state and the operating speed of harvester as the control quantity, the feed rate of harvester was simulated by using the method of model prediction. Grey wolf optimization algorithm was used to optimize the weight matrix of quadratic programming. The simulation results showed that the MAE of feed rate control was less than 0.1kg/s after the weight matrix optimization, which was decreased by 38.1% on average. The error of feed rate control was inversely proportional to the yield of the harvest area and proportional to the moisture content. The control effect was better in harvest areas with even wheat growth.