Position Tracking System of Filling Machine Based on Compound Control Strategy

Abstract

In view of the complexity and periodic motion of automatic filling machine, a novel compound control strategy based on single neuron PID model reference adaptive control and repetitive control is proposed. Diagonal recurrent neural network (DRNN) is used as on-line identifier of system for the single neuron PID controller to adjust its weights and PID parameters by self-learning and self-adapting. The dynamic state performance can be improved by adaptive PID controller based on DRNN on-line Identification and the steady state performance is improved by modified repetitive controller. Computer simulation results show that the control system has good ability of restraining disturbances and high position tracking precision and good robustness. The reliability of whole system is further improved.