Neural network control approach for a two-tank system

Abstract

This paper describes two different approaches in a two tank-system control using neural networks - the NARMA-L2 Control and the Model Reference Control. Knowing that the process control has had the most satisfying results using a standard PID controller, this paper compares the two approaches’ results one to another but also every one of them with the PID controller’s results. The goal was to increase the system response speed without heavily increasing the two other relevant parameters - the overshoot and the steady state error. All of the experiments, measurements and simulations were conducted in Matlab/RT Simulink.