System identification and modeling for interacting and non-interacting tank systems using intelligent techniques

TL;DR

This paper explores various system identification techniques, including statistical, genetic, neural network, and fuzzy logic methods, to model interacting and non-interacting tank systems based on experimental data.

Contribution

It introduces a comprehensive framework applying multiple intelligent and statistical methods for system identification tailored to tank processes with real-time data.

Findings

Effective transfer function models obtained

Neural network and fuzzy models capture nonlinear behaviors

Genetic algorithms optimize model parameters

Abstract

System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.