蔬菜移栽机取苗机构运动控制系统研究

发布时间：2018-03-19 08:40

本文选题：取苗装置　切入点：数学模型　出处：《西南大学》2017年硕士论文　论文类型：学位论文

【摘要】：目前,我国的蔬菜移栽方式主要是人工移栽与半自动化移栽,其劳动强度大,生产效率低等缺点一直制约着我国蔬菜行业的发展。提高蔬菜移栽机自动化程度,对于增加蔬菜产量、解放劳动力以及提高生产效率有着十分重要的意义。而蔬菜移栽机自动化的关键技术在于取苗过程自动化,取苗过程自动化包括取苗点定位、夹持秧苗、投放秧苗三个过程,本文在现有蔬菜移栽机取苗装置的基础上,重点针对取苗点定位过程开展了一系列研究,设计了一套基于单神经元PID算法的取苗点定位过程的控制系统。本文的主要研究内容有:(1)根据已有取苗装置的实际情况,对装置的执行机构及传感器进行数学建模。装置执行机构包括使装置转动的步进电机以及推动装置达到取苗点的双轴气缸;装置使用的传感器包括测量步进电机转动角度的角度传感器以及测量双轴气缸伸长量的位移传感器。根据执行机构和传感器的功能,分别建立以步进电机与角度传感器为被控对象的传递函数,以双轴气缸和位移传感器为被控对象的传递函数。根据穴盘传输装置及取苗装置的运动情况,对其运动过程分别进行分析,得到关于步进电机转动角度与双轴气缸伸长量的运动参数。(2)以取苗装置中的步进电机与双轴气缸为控制对象,设计了单神经元PID控制算法。在Simulink中对不同的执行机构及传感器所组成的被控对象,分别进行建模仿真分析,并与传统PID控制算法进行对比。仿真结果表明:单神经元PID控制算法比传统PID控制算法精度高,超调量小,系统性能更优异。(3)以STM32F103C8T6为控制核心,结合单神经元PID控制算法,搭建控制系统所需的硬件和软件平台进行试验,记录分析试验数据。最终结果表明:采用单神经元PID算法对取苗装置取苗点定位的过程中,步进电机角度最大误差绝对值不超过3%,双轴气缸取苗位移最大绝对值误差不超过5%。取苗装置在实际运动过程中,经过加速度传感器测得,在x轴方向上加速度最大振幅减小约为0.1g,y轴方向上加速度最大振幅减小约为0.22g,z轴方向上加速度最大振幅减小约为0.18g。其振动减小较为明显,系统的稳定性得到提高,能够很好的满足实际工作需求。同时也说明,在现有蔬菜移栽机取苗装置平台上采用单神经元PID控制算法是可行的。
[Abstract]:At present, the main ways of vegetable transplanting in our country are manual transplanting and semi-automatic transplanting. Its disadvantages, such as high labor intensity and low production efficiency, have been restricting the development of vegetable industry in our country. It is very important for increasing vegetable yield, liberating labor force and improving production efficiency. The key technology of vegetable transplanter automation lies in the automation of seedling collection process, which includes the location of seedling point and the holding of seedling. On the basis of the existing vegetable transplanting machine, this paper carried out a series of research on the positioning process of the seedling point. A control system based on single neuron PID algorithm is designed to locate the seedling point. The main research content of this paper is: 1) according to the actual situation of the existing seedling collection device, The actuator and sensor of the device are modeled mathematically. The actuator of the device comprises a stepping motor for rotating the device and a two-axis cylinder for driving the device to reach the seedling point. The sensors used in the device include an angle sensor for measuring the rotation angle of a stepping motor and a displacement sensor for measuring the elongation of a biaxial cylinder. The transfer function of stepper motor and angle sensor is established, and the transfer function of two-axis cylinder and displacement sensor is established respectively. Based on the analysis of the motion process, the motion parameters of the step motor rotation angle and the two-axis cylinder elongation are obtained. The stepper motor and the two-axis cylinder in the seedling device are taken as the control objects. A single neuron PID control algorithm is designed. The controlled objects composed of different actuators and sensors are modeled and simulated in Simulink. Compared with the traditional PID control algorithm, the simulation results show that the single neuron PID control algorithm has higher precision, less overshoot and better system performance than the traditional PID control algorithm. The hardware and software platform needed for the control system is built to test and record the test data. The final results show that the single neuron PID algorithm is used to locate the seedling point of the seedling sampling device. The absolute value of the maximum angular error of the stepping motor is not more than 3 and the maximum absolute error of the displacement of the two-axis cylinder is not more than 5. The maximum amplitude of acceleration in the direction of x axis is reduced by about 0.1 g / y axis direction, and the maximum amplitude of acceleration in the direction of 0.22 g / z axis decreases to about 0.18 g. The vibration decreases obviously and the stability of the system is improved. At the same time, it shows that it is feasible to adopt single neuron PID control algorithm on the platform of seedling collecting device of vegetable transplanter.
【学位授予单位】：西南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：S223.9

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