IMPROVE SYSTEM STABILITY USING NEURAL HYBRID CONTROLLER-PLC

Abstract

This paper presents methods for controlling a real model
using the S7-400 controller with SCL language (Structured Control Language). The two controllers designed are the hybrid Neural Network Neural-PID (Proportional Integral Derivative) controller and the RBF (Radial Basis Function)- PID controller. The control results of the real model, a single water tank, give quite good results, the errors in all cases are
small, and the overshoot is small. In the two cases above, the system operates stably and the Neural-PID hybrid controller gives the best results. The hybrid Neural-PID controller has both the stability of a PID controller and the adaptive learning of a Neural controller. Therefore, this method is capable of controlling other models in industry such as controlling weighing conveyors in the cement industry, controlling heating systems, etc.