摘要
针对丘陵山区现有的自走式施肥机变量控制系统存在惯性大、非线性以及不能及时响应等,传统PID控制策略很难达到精准施肥要求。为此,在建立施肥控制系统数学模型的基础上,采用模糊PID对排肥轴转速进行控制,然后在Simulink工具箱搭建该控制系统的PID仿真模型。分析、对比传统参数整定的PID控制和自适应模糊PID控制系统性能差异。模型仿真和田间试验结果表明:自适应模糊PID控制器改进后的系统模型,响应时间为0.7 s,超调量3.36%,相比传统PID控制模型具有更好的动静态特性;而且在排肥控制性能试验中,单穴排肥量误差为1.52%~5.10%,变异系数最大为4.31%,排肥量准确性和均匀性均达到要求,改进的控制系统性能更优。
For the existing variable control system of self-propelled fertilizer applicator in hilly and mountainous areas, there are problems such as large inertia, nonlinearity, and inability to respond in time. Traditional PID control strategies are difficult to meet the requirements of precise fertilization. For this reason, based on the establishment of the mathematical model of the fertilization control system, fuzzy PID is used to control the speed of the fertilizer shaft, and then a PID simulation model of the control system is built in the Simulink toolbox. Analyze and compare the performance differences between traditional parameter tuning PID control and adaptive fuzzy PID control system. Model simulation and field test results show that the system model improved by the adaptive fuzzy PID controller has a system response time of 0.7 s and an overshoot of 3.36%, which has better dynamic and static characteristics than the traditional PID control model;In the fertilizer control performance test, the error of the single-hole fertilizer output is 1.52%-5.1%, and the maximum variation coefficient is 4.31%. The accuracy and uniformity of the fertilizer output meet the requirements, and the improved control system has better performance.
作者
肖远
吴雪梅
宋朱军
张富贵
符德龙
Xiao Yuan;Wu Xuemei;Song Zhujun;Zhang Fugui;Fu Delong(School of Mechanical Engineering,Guizhou University,Guiyang City,Guizhou Province 550025,China;Bijie Branch of Guizhou Provincial Tobacco Corporation,Bijie City,Guizhou Province 551700,China)
出处
《农业装备与车辆工程》
2022年第10期42-46,共5页
Agricultural Equipment & Vehicle Engineering
基金
贵州省烟草公司毕节市公司科技项目(毕烟技[2021]18号202152500240048)
贵州省农业产业技术体系建设专项经费资助
贵州省科技计划项目(黔科合支撑[2017]2595)。
关键词
变量施肥
自适应模糊PID控制
固体施肥
变异系数
variable fertilization
adaptive fuzzy PID control
solid fertilization
coefficient of variation