Design of neural network and PLC-based water flow controller

Abstract

Flow rate is a fundamental physical quantity in the fluid transportation system from one place to another. To achieve this, a reliable controller that is able to produce a constant flowrate in industry is needed. The most used flow controllers in industries are PID-based controllers that are implemented using PLCs. However, there are still shortcomings, they can perform poorly in some applications, for example in the highly nonlinear system which cannot be overcome by conventional PID controllers. There are some other limitations of PID controller, such as PID has the overshoot and undershoots in the output of controlled system and PID gives late response in this study, a neural network-based flow controller is proposed to deal with that problems. The controller will be operated in a miniature plant which consists of a water tank, water pump, a control valve, and a flow transmitter. Due to PLC limitation that cannot be programmed with common programming languages such as MATLAB, a personal computer (PC) is used to run the proposed neural network controller. The PC communicates with the PLC using OPC (OLE for Process Control) server, while the PLC reads the flow transmitter and also controls the control valve directly based on the result output of the neural network controller. In order to evaluate the performance of the proposed controller, several experiments have been conducted. The performance of the proposed controller has been compared with the conventional PID controller. It shows that neural network-based controller outperformed the conventional PID controller, in terms of maximum overshoot and steady-state error, where the neural network controller has maximum overshoot = 5.36% and steady-state error = 0.85%, while the PID controller has 11.3% for overshoot and 1.10 % for steady-state error.