An improved flush material belt weigh feeder system via fuzzy logic controller and adaptive neural networks

The Flush Material Belt Weigh Feeder (FMBWF) has used in many material handling plants. The stability and the performance of the layer control system will affect the quality of the production. In general, the behavior of the flush material on the BWF is non-linear, time-lag, and disturbance character. The layer of the flush material on the belt is hard to be stably controlled especially the occurrence of unstable situation in flush material or the pre-feeder feeding rate and the variation of the set point value of the FMBWF. This paper focuses on the performance improvement of the FMBWF via adopted a fuzzy controller under the situation of the Flow Rate Set Value (FRSV) variation. The proposed fuzzy controller is utilized instead of the original feed forward compensation function to deal with the problems of the temporary unstable situation. This paper offers the simulation result comparison of the set point value variations between the BWF for the original and the proposed control system. The simulation results are distinct better than the original control system. The influence of the variations of the set point value can be successfully evaluated by the Fuzzy controller and adjusted the Proportional Actuator immediately. Finally the proposed control system remains convergence with smooth and stable. Therefore, the accuracy, stability and the performance of the control system are improved.