Control Of Injection Speed Based On Nonlinear Regression Predictive And Iterative Learning

Injection molding is a typical batch process. With the energy shortage and the good quality of the plastic products, injection molding is getting more and more attention. Injection Molding Machine (IMM) is one of the most important equipment in plastic industry. It has developed rapidly in the control technology rather than in the structure and transmission, since it appeared in the3rd decade of last century. So research on the control method of IMM is significant for improvement of plastic product and progress of plastic industry.In the process of injection molding, the speed of injection is the key to the quality of the products. So whether controlling the injection speed effectively or not is of value in use. The injection process is a non-linear process. And it is difficult to get an accurate mathematical model of the injection process. In view of the above character of the injection process, it is hard to satisfy the control demand for the traditional control algorithm. Besides, the injection processes is a repeat production process.On the basis of good understanding of the predict control theory and dynamic matrix algorithm, this paper is set in the injection molding process in the211laboratory of Northeastern University. Aiming at the shortage of the traditional predicts control in handling of nonlinear object and industrial applications, this paper makes a research on the predictive control based on nonlinear regression. And the simulation based on a nonlinear process has verified the advantage of the algorithm. Secondly, according to the repeatability and batches of the injection process a new controller based on the prediction of nonlinear control and the iterative learning algorithm is established. Then the controller is applied to the model of injection velocity, and it has received perfect control effects.At the same time, in this paper the design and implementation of the PLC control system and the monitoring picture for the system are also introduced briefly. In the end, on the basis of a summary for the research work, a feasible improvement measure is put forward according to the shortages in the existed method, and the future research trend is discussed.