【作者】 李伟东；

【导师】 李笑；

【作者基本信息】 广东工业大学 ， 机械工程（专业学位）， 2022， 硕士

【摘要】 气压传动设备由于元件结构简单、抗环境污染、抗干扰性强、成本低廉、功率体积比高和便于集中供气等特点,已成为现代社会重要的工业设备之一。电-气比例位置控制系统作为气压传动设备的典型应用,已广泛用于包装、机械、印刷、电子等多种行业。但是由于受气体的可压缩性、容腔和负载变化等因素的影响,电-气比例位置控制系统本质上是一类非线性、时变系统,给电-气比例位置控制系统的控制带来了难度。常规定增益PID控制,由于控制器参数难以整定且不能适应系统参数变化,难以满足现代工业设备的定位控制精度要求。为了解决电-气比例位置控制系统PID控制的定位精度问题,本文设计了一种基于负载补偿的电-气比例位置控制系统模糊神经网络(FNN)控制结构和算法,在常规PID控制的基础上结合了模糊控制的仿人思维和神经网络的自适应性,解决了常规PID控制因参数固定难以适应非线性、时变系统动态变化的问题,仿真和实验验证了控制算法的有效性。本文的主要研究工作如下:(1)分析了系统的非线性和时变因素,建立了电-气比例位置控制系统的数学模型,依据实验设备参数建立了实验平台的参数化数学模型,并验证了其稳定性。(2)分析了常规PID控制策略的局限性,提出了以设定位移和气缸压力为输入,以PID控制器输出作为学习信号的FNN控制策略,设计了一种基于负载补偿的电-气比例位置控制系统FNN控制结构和算法。(3)利用AMESim建立了电-气比例方向控制阀控制气缸(阀控缸)的数学模型,利用Simulink建立了控制系统并用S函数编写了控制算法,仿真验证了FNN结构和算法能有效减小常规PID控制的误差。(4)搭建电-气比例位置控制系统实验平台,编写Lab VIEW测控程序,分别实验研究了系统在正弦波、三角波、随机波形和阶跃信号输入时,在气缸速度、负载力和负载容腔变化干扰作用下FNN控制与常规PID控制的轨迹跟踪特性,以及不同位移输入和负载作用下的阶跃响应特性。实验验证了FNN结构和算法能有效补偿负载容腔变化、负载力变化引起的误差且提高气缸的响应速度。更多还原

【Abstract】 Pneumatic transmission equipment has become one of the important industrial equipment in modern society because of its features of simple component structure,anti-environmental pollution,strong anti-interference,low cost,high power volume ratio and convenient centralized gas supply.As a typical application of pneumatic transmission equipment,electro-pneumatic proportional position control system has been widely used in packaging,machinery,printing,electronics and other industries.However,due to the influence of gas compressibility,cavity and load variation,the electro-pneumatic proportional position control system is a kind of nonlinear and time-varying system in nature,which makes it difficult to control the electro-pneumatic proportional position control system.Gain PID control is usually prescribed,but it is difficult to meet the positioning control accuracy requirements of modern industrial equipment because the controller parameters are difficult to set and cannot adapt to the system parameter changes.In order to solve the electrical and pneumatic proportional position control system of PID control precision problem,this thesis designs a load compensation electrical and pneumatic proportional position control system of fuzzy neural network(FNN)control structure and algorithm,on the basis of the conventional PID control is a combination of fuzzy control of humanoid thought and the adaptability of neural network,It solves the problem that conventional PID control is difficult to adapt to the dynamic change of nonlinear and time-varying system because of fixed parameters,and the effectiveness of the control algorithm is verified by simulation and experiment.The main research work of this thesis is as follows:(1)The nonlinear and time-varying factors of the system are analyzed,the mathematical model of the electric-gas proportional position control system is established,and the parameterized mathematical model of the experimental platform is established according to the experimental equipment parameters,and its stability is verified.(2)The limitation of the conventional PID control strategy is analyzed,and the FNN control strategy is proposed,which takes setting displacement and cylinder pressure as input and PID controller output as learning signal,and a control structure and algorithm of the electro-pneumatic proportional position control system FNN based on load compensation is designed.(3)The mathematical model of electro-pneumatic proportional directional control valve control cylinder(valve-controlled cylinder)is established by AMESim.The control system is established by Simulink and the control algorithm is written by S function.The simulation verifies that the FNN structure and algorithm can effectively reduce the error of conventional PID control.(4)Build the experimental platform of electro-pneumatic proportional position control system,write the Lab VIEW measurement and control program,and experimentally study the trajectory tracking characteristics of FNN control and conventional PID control under the interference of cylinder velocity,load force and load cavity changes when sinusoidal wave,triangular wave,random waveform and step signal are input.Experimental results show that the FNN structure and algorithm can effectively compensate the error caused by the variation of load cavity and load force.更多还原