Application of Neural Networks Based SANARX Model for Identification and Control Liquid Level Tank System

This paper is devoted to application of artificial Neural Network based Simplified Additive Autoregressive exogenous model for identification and control of a liquid level tank system consisting of three water reservoirs. A specific restricted connectivity structure of the neural network is trained on input-output data set to identify a nonlinear dynamic single-input single-output model of the liquid level tank system. Parameters of the identified neural network based model can be used to design a dynamic controller for the system. The designed neural network based controller is verified on mathematical model inMATLAB/Simulink environment and applied to the real-time control of the plant. The goal of the control algorithm is to track the desired level of liquid in the upper tank. Experimental result have shown a very good performance of the proposed technique. The designed nonlinear controller is capable of tracking the desired water level for all set points with high degree of accuracy, maximally fast and without significant overshoot.