Comparative Analysis of ANN based Intelligent Controllers for Three Tank System

Full Text (PDF, 637KB), PP.34-41

Views: 0 Downloads: 0

Author(s)

Kodali Vijaya Lakshmi 1,* Paruchuri Srinivas 1 Challa Ramesh 2

1. Department of EIE, VR Siddhartha Engineering College, Vijayawada, 520007, India

2. Department of EIE, Bapatla Engineering College, Bapatla, 522101, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2016.03.04

Received: 28 Jun. 2015 / Revised: 1 Oct. 2015 / Accepted: 1 Jan. 2016 / Published: 8 Mar. 2016

Index Terms

Three tank system, ANN, Intelligent controllers, Model predictive, Model reference, NARMA-l2

Abstract

Three tank liquid level control system plays a significant role in process industries and its behavior is nonlinear in nature. Conventional PID controller generally does not work effectively for such systems. This paper deals with the design of three intelligent controllers namely model predictive, model reference and NARMA-L2 controllers based on artificial neural net-works for a three tank level process. These controllers are simulated using MATLAB/SIMULINK. The performance indices of intelligent controllers are compared based on the time domain specifications. The performance of NN predictive controller shows superiority over other controllers in terms of settling time.

Cite This Paper

Kodali Vijaya Lakshmi, Paruchuri Srinivas, Challa Ramesh, "Comparative Analysis of ANN based Intelligent Controllers for Three Tank System", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.3, pp.34-41, 2016. DOI:10.5815/ijisa.2016.03.04

Reference

[1]P.Srinivas, K.Vijaya Lakshmi & V. Naveen Kumar, “A Comparision of PID Controller tuning methods for three tank level process”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering(IJAREEIE), Vol. 3, Issue 1, Jan. 2014.
[2]Nazmul Siddique, Hojjat Adeli, “Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing”, Wiley publication, Edition 2013.
[3]Demuth and Beale, “MATLAB Neural Network Tool Box User’s Guide”, Version 8.3, Massachusetts: The Mathworks Inc., 2015.
[4]Kadam D. B., Patil A. B., & Paradeshi, K. P., “Neural Network based Intelligent Process Control System”, International Conference on Recent Trends in Information, Telecommunication and Computing (ITC), (pp. 356-358), IEEE 2010.
[5]A.Nikfetrat, A.R.Vali, V.Babaeipour,”Neural Network Modeling and Nonlinear predictive control of a Biotechnological Fed Batch Process”, IEEE International Conference on Control and Automation, December, 2009.
[6]Xiongwei Shi, Junfei Qiao, “Neural Network Predictive Optimal Control for Waste water treatment”, International Conference on Intelligent Control and Information Processing, August 2010, China.
[7]Narinder Singh and Bharti Panjwani, “Comparison of Neural network and Fuzzy logic control for Nonlinear model of Two link rigid manipulator”, International Journal of Control and Automation, Vol.7, No.4,pp.417-428,2014.
[8]Mojtaba Rostani Kandroodi and Behzad Moshiri, “Identification and Model Predictive Control of Continuous stirred tank reactor based on Artificial Neural Networks”, International Conference on Control, Instrumentation and Automation, pp.338-343, 2011.
[9]Masoud Salehi Borujeni, Hassan Zarabadipour, “Fuel cell Voltage control using Neural Network based on Model Predictive Control’, Iranian conference on intelligent systems, 2014, IEEE.
[10]Ayachi Errachdi, Mohamed Benrejeb, ”Model Reference Adaptive Control based on Neural Networks for Nonlinear time varying system”, International Conference on Systems, Control and Informatics,pp.73-77, 2013.
[11]R.Prakash, R.Anita, “Neuro-PI controller based Model Reference Adaptive Control for Nonlinear systems”, International Journal of Engineering, Science and Technology, Vol.3, No.6, pp.44-60, 2011.
[12]Bharti Panjwani, Vijay Mohan, “Comparative Performance Analysis of PID BasedNARMA-L2 and ANFIS Control for Continuous Stirred Tank Reactor”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-5, November 2013.
[13]S.Janani, C.Yasotha, “Design and Analysis of Neuro Controller based on Narma-L2 Model”, International Journal of Adavanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, Issue 4, April 2014.
[14]Farhan A Salem, Ayman A Aly, “PID Controller Structures: Comparison and selection for an electromechnical system", International Journal of Intelligent Systems and Applications, Vol.7, No.2, 2015.
[15]Parikshit Kishor Singh, Surekha Bhanot, Hare Krishna Mohanta, “Optimized and self-organized fuzzy logic controller for pH Neutralization process”, International Journal of Intelligent Systems and Applications, Vol.5, No.12, 2013.