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基于PID的直线电机控制方法及实验研究

发布时间:2018-03-16 02:24

  本文选题:模糊PID 切入点:前馈控制 出处:《合肥工业大学》2014年硕士论文 论文类型:学位论文


【摘要】:高速高精度运动平台被广泛应用于高端数控装备,如高档数控加工中心、航空航天制造装备和IC封装设备等。随着科技的发展,对高端数控装备的要求也越来越高,给高速高精平台及其伺服控制的设计带来了极大的挑战。因此本文面向高端数控装备,对高速高精直线电机驱动技术及其控制方法展开了研究,这对加快我国制造产业的发展具有重要的理论意义与实际应用价值。 本文针对直线电机数控系统实验平台,对其系统误差进行补偿,并重点研究了基于PID的直线电机控制方法,提出了带前馈模糊PID控制方法,并利用直线电机数控系统实验平台展开实验,,成功地验证了带前馈模糊PID控制方法的优越性。本文的主要内容及成果有以下几点: (1)掌握直线电机高速高精平台的结构组成和驱动特点,并建立永磁同步直线电机的数学模型。同时为了减少由于制造和装配等引起的系统误差,采用分段线性误差补偿模型对直线电机数控系统实验平台的系统误差进行一次性补偿。 (2)对直线电机的PID、带前馈的PID和模糊PID控制方法进行了深入研究,并提出了一种适用于直线电机伺服控制的带前馈模糊PID控制方法。 (3)分别建立了基于PID、带前馈的PID、模糊PID和带前馈模糊PID控制方法的直线电机伺服系统控制模型,并在MATLAB/SIMULINK软件平台上建立了对应的仿真模型,进而对四种控制方法分别开展了单位阶跃响应和正弦响应仿真实验。结果表明,带前馈模糊PID控制方法的响应速度快且控制精度高,较优于其它三种控制方法。 (4)设计并编写出了基于PID、带前馈的PID、模糊PID和带前馈模糊PID的直线电机伺服控制算法,并利用PMAC运动控制卡,实现各种控制方法对直线电机的控制。利用上述四种控制方法,在变负载下对直线电机进行控制实验,将带前馈模糊PID控制方法与其它三种控制方法的控制效果进行分析对比,结果表明:带前馈模糊PID控制方法既可避免静态误差,减小跟踪误差,又不依赖于精确的控制模型,可根据工作环境实时地改变控制参数,具有很强的适应性,且实验结果与理论分析及仿真结果具有较好一致性。
[Abstract]:High-speed and high-precision motion platform is widely used in high-end numerical control equipment, such as high-grade NC machining center, aerospace manufacturing equipment and IC packaging equipment, etc. With the development of science and technology, the requirements for high-end numerical control equipment are becoming higher and higher. It brings great challenge to the design of high speed and high precision platform and its servo control. Therefore, the driving technology and control method of high speed and high precision linear motor are studied in this paper. This has important theoretical significance and practical application value to accelerate the development of our country's manufacturing industry. In this paper, aiming at the experiment platform of linear motor numerical control system, the system error is compensated, and the control method of linear motor based on PID is studied, and the fuzzy PID control method with feedforward is proposed. The advantages of fuzzy PID control method with feedforward are successfully verified by using the experiment platform of linear motor numerical control system. The main contents and results of this paper are as follows:. 1) mastering the structure composition and driving characteristics of the high speed and high precision platform of linear motor, and establishing the mathematical model of permanent magnet synchronous linear motor. At the same time, in order to reduce the system error caused by manufacture and assembly, etc. The piecewise linear error compensation model is used to compensate the system error of the experiment platform of linear motor numerical control system. In this paper, the PID and fuzzy PID control methods with feedforward for linear motor are studied, and a fuzzy PID control method with feedforward for linear motor servo control is proposed. The control model of linear motor servo system based on PID-PID-feedforward, fuzzy PID and feedforward fuzzy PID control method is established, and the corresponding simulation model is established on the platform of MATLAB/SIMULINK software. The simulation experiments of unit step response and sinusoidal response of four control methods are carried out respectively. The results show that the fuzzy PID control method with feedforward is faster in response speed and higher in control accuracy than the other three control methods. The servo control algorithm of linear motor with feedforward, fuzzy PID and feedforward fuzzy PID is designed and written, and the PMAC motion control card is used to realize the control of linear motor. In the control experiment of linear motor under variable load, the control effect of fuzzy PID control method with feedforward and other three control methods is analyzed and compared. The results show that the fuzzy PID control method with feedforward can avoid static error. The control parameters can be changed in real time according to the working environment, and the experimental results are in good agreement with the theoretical analysis and simulation results.
【学位授予单位】:合肥工业大学
【学位级别】:硕士
【学位授予年份】:2014
【分类号】:TM359.4

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