Support Vector Machine-based Abs Resin Polymerization Temperature Control

Support Vector Machine is a new machine learning method in the field of statistics. It has been used in the field of pattern identify effectively. It is also a powerful tool in nonlinear system discrimination. The support vector machine method combining with radial basis function neural network (SVNN) has been proved no-biased on the modeling of nonlinear system, but SVNN method has seldom been used to solve the problem in industry procedure control by now.The polymerization process of ABS resin is nonlinear and time-varying. It is sensitive to the temperature and the change of temperature, which has higher requirement on system modeling and controller designing.In this paper, SVNN method is used model the polymerization process of ABS resin. Furthermore, combing SVNN method with adaptive inverse control method, this paper proposes a new adaptive inverse control method for nonlinear system. By this method, an inverse model of polymerization process of ABS is proposed; the SVNN is trained using data from the production field. This inverse model is used as a controller and its output is used to the controlled object. Meantime, the output error is used to adjust the input of the inverse model controller by the PID method, and the correction of the inverse model controller is realized at the same time. The results of the simulation show that the precision of this method higher than normal neural network, furthermore, the SVNN method shortens the training time and improves the generalization ability. The adaptive inverse control arithmetic based on the support vector neural network has characters of simplicity, credibility, effectivity and robustness. Compared with the normal neural networks controller, the proposed method can adapt the change of model by adjusting the parameters of controller directly, and shows stronger robustness; compared with the conventional adaptive control system, the method proposed in this paper needs no precise mathematical model, so it has extensive application.This paper's work shows that support vector neural network can not only be used in solving the modeling problem such as temperature control of the ABS resin polymerization process successfully, but also provide a powerful tool in the complicated industry process control.