Single Neuron PID Adaptive and Repetitive Control for Filling Machine Position Tracking System

摘要

In view of the complexity of controlled plant and periodic motion of the horizontal position tracking system in automatic filling machine, a novel approach based on single neuron PID model reference adaptive control and repetitive control for AC permanent magnet synchronous motor (PMSM) control system is proposed. Radial basis function (RBF) neuron network is used to identify the system on-line for the single neuron PID controller to adjust its weights and PID parameters by self-learning and self-adapting based on the desired output from a reference model. The dynamic state performance can be improved by the single neuron adaptive PID control and the steady state performance is also improved by modified repetitive control. Computer simulation results show that the control system has fine dynamic and steady state performance, and high position tracking precision, and good robustness. The reliability of whole system is further improved.