Adaptive Decoupling Predictive Temperature Control Using Neural Networks for Extrusion Barrels in Plastic Injection Molding Machines

In the paper, an adaptive decoupling predictive temperature control using neural networks (NN) is presented for extrusion barrels in plastic injection molding machines. Due to weakly coupling effects, the extrusion barrels are approximated by decoupling linear system models together with independent NN models. These decoupling system parameters and NN models are experimentally determined using the recursive least-squares estimation (RLSE) approach with forgetting factor. The adaptive decoupling predictive PI control laws together with NN compensating terms are developed by minimizing a generalized predictive cost function. A real-time control algorithm is then proposed to achieve temperature control of extrusion barrels. Experimental results on a laboratory-built extrusion barrel are conducted to illustrate the usefulness and applicability of the proposed method are well exemplified by conducting.