基于鲁棒PID控制器的注射机料筒温度控制系统

注射机的料筒温度是决定塑料成品品质的一个关键因素。基于一个假设,即存在一个稳定的实主导极点,并且这个极点的阶数超过所设计的PID控制器的可调整参数的数量。提出了一种新的基于多主导极点方法的PID控制器,研究了注射机料筒温度的一阶积分时滞系统的鲁棒控制。利用Matlab求解特征方程对s的各阶导数组成的联立方程组,得到主极点和PID控制器的可调参数。在注射机注射速度系统的一阶积分时滞系统下进行了仿真实验,结果表明:设计方法具有良好的追踪性能和调节性能,并且鲁棒性良好,能够满足注塑机料筒温度控制的需求。     

基于自适应模糊PID的注塑机温度控制及仿真

为了对注塑机温度精确控制,针对注塑机温度在传统比例积分微分(PID)控制系统中存在的控制偏差大、调节速度慢等诸多不足的现状,提出将模糊控制器和PID控制器相结合,构造成一个自适应模糊PID控制器,运用MATLAB软件建立传统PID和自适应模糊PID控制器模型,对机筒温度进行仿真对比。设定200℃为给定温度,最终仿真结果显示,与传统PID控制器相比,自适应模糊PID控制器实现了注塑机温度的实时控制,达到了超调量小,升温快速及稳态无误差等要求。表明了自适应模糊PID控制器比传统PID控制器对温度的控制效果更加良好。     

微结构注塑机注射速度控制及仿真研究

为了对注射速度进行精密控制,建立起一个液压控制系统,对该系统中影响注射速度的相关器件进行结构分析并且建立数学模型,针对注射速度非线性以及时变等特性,设计一种自适应模糊PID控制器,利用MATLAB软件建立仿真模型,对两种控制器下的注射速度曲线进行对比分析。结果表明,与传统PID控制器相比,自适应模糊PID控制器具有注射速度响应更迅速,稳态无误差和鲁棒性能好等优势。     

基于LabVIEW的注射速度闭环PD型迭代学习控制器设计

针对注塑机注射速度的非线性、时变性和重复性等特点,提出了注射速度的闭环PD型迭代学习控制器。该控制器使用系统实际输出与期望目标之间的偏差修正不理想的控制信号,若干次迭代后系统能够实现期望轨迹的跟踪。利用Lab VIEW软件中丰富的控制系统函数控件建立系统仿真程序,以生产一次性薄壁饭盒盖所需注射速度进行仿真实验。结果表明,该控制器能有效跟踪期望的注射速度轨迹,动态响应速度快,超调量小,调整时间短,控制效果优于常规PID控制器。     

GAAS优化PID算法在全电动注塑机注射系统中的应用

全电动注塑机有2个伺服电机同时进行驱动,与一般的伺服液压注塑机相比,控制精度更高、控制速度更快,针对其控制系统提出一种遗传算法修正的蚁群算法(GAAS)对闭环PID控制的控制参数进行优化,该算法能够有效提高系统的快速性和准确性。Rosenbrock函数验证了嵌入遗传算法后蚁群算法的优越性,嵌入遗传算法后收敛速度更快,算法具有更优良的收敛速度。GAAS算法能够提供更加准确可靠的PID参数,且不会降低系统的调节性能。仿真实验证明,GAAS算法能够有效减小系统超调量、缩短调节时间;在实际运行中,注射螺杆的控制精度在0. 5%以内。因此,控制器可以大幅度改善全电动注塑机的闭环控制效果,参考价值较大。     

基于分段PID的注塑机料筒温度控制算法研究与仿真

为了提高注塑机料筒温度控制器的适用范围和控制精度,针对目前同一型号注塑机在不同生产环境和工况条件下,根据生产原料不同,最佳注射温度都需要调整,而单一固定参数PID温度控制器超调有时过大、稳态误差有时不符合注塑要求的情况。基于现场PID参数调试人员的经验提出分段PID料筒温度的控制方法,并且基于Lab VIEW与MATLAB混合编程技术,建立了仿真系统。通过仿真测试研究表明:分段PID料筒温度控制比固定参数PID控制适用性强,控制效果更好。     

基于BP神经网络PID的注塑机液压控制

注塑机液压系统是一个非线性、大时滞性和强耦合的复杂系统,传统比例积分微分(PID)控制由于参数固定不变,导致超调量大、稳定性差、控制精度低,对注塑机液压控制效果不理想,现提出了一种改进型误差反向传播法(BP)神经网络PID控制方法。分析了液压注塑机工艺流程以及液压伺服系统控制原理,在此基础上将BP神经网络与PID控制方法相结合,通过神经网络的自学习、加权系数的调整,优化PID控制器参数K_i、K_p、K_d,并将粒子群算法引入到神经网络中作为其学习算法,以有效提高BP神经网络算法的收敛速度。仿真结果表明,经过粒子群优化后的BP神经网络能够快速地对PID参数进行自适应调整,同时粒子群优化后的BP神经网络控制效果明显优于传统PID控制,该控制方法对于提升注塑机液压系统响应速度以及控制精度具有重要作用。     

基于单神经元自适应PID控制的注塑机合模机构

设计了一个单神经元自适应比例积分微分控制器(PID)控制器,使用AMESim/Simulink联合仿真机制,建立了注塑机合模机构液压伺服系统的模型。由仿真结果看,与常规PID控制器相比,使用单神经元自适应PID控制器控制的系统有着更好的鲁棒性,系统获得了更好的环境适应性。     

基于PLC的注塑机多段温度控制系统设计

为保证注塑机注塑质量和加工精度,应对注塑机料筒温度进行精确控制,因此设计了基于可编程控制器(PLC)为核心控制器的注塑机温度控制系统。首先介绍了注塑机结构和注塑工艺流程,以PLC、触摸屏为控制器核心设计了注塑机硬件系统。采用热电偶传感器采集各段温度并传送到PLC中,通过比例积分微分(PID)智能控制算法完成温度的闭环精确控制。结果表明:基于PLC的注塑机PID多段温度控制系统能够实现温度的精确控制,温度误差能控制在±0.3℃以内;该控制系统完全满足注塑工艺要求,能够显著提高注塑机的自动化程度。     

基于模糊自适应PID的注塑机机筒温度实时控制

为了提高注塑机机筒温度控制的稳定性、提高机筒温度控制精度,提出了一种模糊自适应比例积分微分(PID)的注塑机温度实时控制方法。采用热电偶传感器对机筒温度信号进行采集,并将其传送到控制器中通过模糊自适应PID控制运算,将采集信号进行数模转换后,自动地对加热系统进行精确控制。最后,利用Matlab软件对模糊PID和传统PID控制系统进行了仿真对比,并对温度实时数据进行了分析。结果表明:模糊PID控制系统动态响应更快、稳态性能更好,实际温度控制精度更高。     

Optimal anticipatory control of ram velocity in injection molding

This paper discusses a computer control system for ram velocity of an injection molding machine using optimal state feedback based on the linear quadratic control theory. A new approach for the selection of appropriate weighting matrices is presented in this context. The simulation results reveal that the optimal controller has improved performance over the conventional PID controller presently used, having faster speed of response, significantly better tracking performance, and better noise filtering properties. The execution speed and the core storage requirements would allow implementation even on a small online computer.     

Control of injection velocity using a fuzzy logic rule-based controller for thermoplastics injection molding

Injection velocity to a large degree determines the melt injection rate during the injection phase, and it has critical impact on the molded part quality, such as shrinkage, warpage, and impact strength. An injection molding machine operates with different injection velocity profiles, barrel temperatures, molds, and materials. These strongly different molding conditions cause the injection velocity dynamics to very significantly and to make the control performance of the injection velocity poor with a typical PID controller. A real-time, closed-loop feedback and feedforward control system based on fuzzy logic has been designed, developed, and implemented to control the injection velocity. The fuzzy logic rules of the controller are optimized by analyzing phase plane characteristics. The controller output membership functions are optimized based on a 2^k factorial design technique. The experimental results reveal that the fuzzy logic-based controller works well with different molds, materials, barrel temperatures, and injection velocity profiles, indicating that the fuzzy logic controller has superior performance over the conventional PID controller in response speed, set-point tracking ability, noise rejection, and robustness.     

注塑机塑化能力影响因素研究

通过螺杆结构、操作工艺参数和聚合物种类3个方面对注塑机塑化能力进行了研究,揭示了螺杆类型、转速、螺杆位移、生产背压、成型温度、原料种类、原料熔体流动速率、驱动单元等与塑化能力的关系。结果表明,随着螺杆转速、成型温度的提高,塑化能力有所上升;随着螺杆背压、螺杆位移的变大,塑化能力有所下降。     

精密注塑机的闭环控制技术

分析了精密注塑机的特点及其对控制系统的要求，以料简温度及注射速度的闭环控制为例，阐述了精密注塑机闭环控制系统的构成及工作原理。进一步指出发展精密注塑机控制系统的必要性和紧迫性。     

Evaluation of simple dynamic models and controllers for hydraulic and nozzle pressure in injection molding

Simple pseudo-steady state relations between the hydraulic and nozzle pressures of an injection molding machine were presented and verified experimentally. A simulation study was performed to evaluate the performance of simple controllers using dynamic models developed for the hydraulic and nozzle pressures. The controllers chosen were the discrete proportional, proportional-integral (PI), and proportional-integral-derivative (PID) types, tuned according to the ITAE criterion. The control of hydraulic pressure simulation showed that the PI controller had the best overall performance, whereas the result of nozzle pressure control loop simulation showed that the PID controller performance was better than that of the PI controller. All the controllers, in both loops, gave responses that were about an order of magnitude more rapid than the open loop response.     

Implementation of Multimodel-Based PID and Intelligent Controller for Simulated and Real-Time Temperature Control of Injection Molding Machine

The proposed real-time multimodel for the injection molding process mainly contributes to the barrel temperature control. Good control of the plastic melt temperature is very important for injection molding in reducing the operator setup time, ensuring product quality, and preventing thermal degradation of the melt. The controllability and set points of the barrel temperature also depend on the precise monitoring and control of the plastic melt temperature. Motivated by the practical temperature control of injection molding, this article proposes a multimodel-based proportional integral derivative (PID) control scheme in real-time and the simulation studies of the PID, fuzzy, and adaptive neuro fuzzy inference system (ANFIS) control schemes. The injection molding process consists of three zones, and the mathematical model for each zone is different. The control output for each zone controller is assigned a weight, based on the computed probability of each model, and the resulting action is the weighted average of the control moves of the individual zone controller.