A Study On Iterative Learning Control Of Injection Speed Of Injection Molding Machine

Injection molding is a common process for the production of plastic products,which plays an important role in agriculture,forestry,animal husbandry,fishery,mining,manufacturing,construction and other fields.With the progress of technology,the quality requirements of plastic products in various industries are higher and higher.In a series of injection molding process,injection speed is the key factor affecting the quality of injection products.The traditional PID(Proportion,Integral,Derivative)control method can not accurately track the injection speed,can not achieve the ideal control effect,affect the quality index of injection products,and the iterative convergence speed is slow,which increases the production cost.In order to improve the control performance of traditional PID control method,a closed-loop PD type iterative learning control strategy and an real-time optimization based on modifier adaptation iterative learning control method for injection speed considering model uncertainty are proposed in this thesis.The main research work of this thesis is as follows:1.Firstly,the basic composition,process and control parameters of the injection molding machine are analyzed in depth.The four main stages of the injection molding process are introduced in detail,including the pre molding stage,injection stage,pressure maintaining stage and cooling stage.Secondly,based on the in-depth analysis of the principle of injection molding process,the main control parameters in the injection molding process were discussed,including temperature,pressure,speed and so on.Finally,the nonlinear mechanism model of injection speed is established to simulate the actual injection process.2.Aiming at the problem that the control performance of the traditional PID control method is not ideal,the thesis proposes a closed-loop PD ILC(Iterative Learning Control)strategy with regard to the injection speed of the injection molding machine,and makes use of the established mechanism model of injection speed for injection molding machine to simulate the actual injection process,and then verifies the effectiveness of the algorithm through simulation experiments.The simulation results show that the method can accurately track the desired injection speed curve,and has good stability,and high steady state accuracy.3.Considering that in the actual production process,due to various constraints(such as production cost,random disturbance,etc.),it is inevitable to have the problem of model mismatch,that is to say,it is difficult to establish a process model that exactly matches the actual injection process.In order to compensate the influence of model uncertainty on the control effect,an iterative learning control method for injection speed of injection molding machine with real-time optimization based on modifier adaptation is proposed in this thesis.This method introduces0-order modifier and 1-order modifier including the actual process data,which can make full use of the production data of historical batch to guide the control of the next batch,It can effectively solve the influence of uncertainty on control effect.The simulation results verify the effectiveness of the method.The method can effectively improve the control accuracy,accelerate the convergence speed,and then improve the control effect.