Research on fast self-learning improvement of ADRC control algorithm for film thickness control system

Abstract

In the biaxially stretched film (BOPP) thickness control system, the traditional PID and Active-Disturbance Rejection Controller (ADRC) can’t achieve the ideal control effect. The Smith prediction method is used in the essay to establish a discretization model for the BOPP thickness control system. Combining with BP self-learning algorithm, a fast self-learning improved ADRC control algorithm (FSADRC) is proposed. By means of the additional momentum term and the adaptive learning rate method, the nonlinear combination of the ADRC system is adjusted in real time, the optimal control parameters are found, and the parameters are self-tuned. As a consequence, the improved algorithm is applied to the biaxial tensile film thickness control model. The simulation results show that the method has the advantages of high response speed and strong self-adaptive ability, which can effectively improve the control performance of the BOPP thickness control system.