Research On Iterative Learning Model Predictive Control Strategy For Packing Phase Of Injection Molding Process

With the development of society, the plastic plays an increasingly important role in industry, agriculture, defense and other areas of daily life. The plastic is also increasingly in demand growth. Injection molding is currently the more widely used plastic processing methods, and the main processing equipment for the injection is the injection molding machine. Faced with increasingly fierce market competition at home and abroad, in-depth study of injection molding machine control technology and constantly optimize the process parameters and injection molding method has become the focus of attention of researchers. As a typical batch production process, molding process is made up of injection, packing, plasticization and cooling phases, among which packing phase is one of the most important parts which decide the product quality. So research on this phase has significant meaning for both the quality improvement of plastic product and the process of plastic industry.For the lack of molding and control section of the existing methods in packing phase, this paper presents an orthogonal-parameterized linear time-varying model for the phase and an iterative learning-model predictive control method on previous LTV model.Main research contents and results are as follows:First of all, using characteristics of the tracking trajectory is known and the process is repeated running, the paper presents an orthogonal-parameterized linear time-varying model for packing phase. The model can describe packing characteristics adequately while the method is simple and reliable.Afterward, using the characteristic of the process run repeatedly, the paper comes up with an iterative learning control method based on the LTV model, and proves its convergence. The experiment result shows that the proposed method can realize unbiased tracking the target trajectory compared with normal PID control method.At the end, the paper proposes an iterative learning-model predictive control method based on the LTV model so as to take full advantage of the real-time feedback information of the current running batch. The experiment result shows that the proposed method has better dynamic tracking performance and higher steady state accuracy compared with iterative learning control method.