i-manager Publications

Investigation of Genetic Algorithm Tuned PI Controller for the Non-Linear Conical Tank Level Process

Arivalahan R.*, Balaji S.**, Tamil Arasan P.***

*-***Department of Electrical and Electronics Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

Periodicity:February - April'2019
DOI : https://doi.org/10.26634/jic.7.2.16725

Abstract

Recently, the PID Controllers are widely used in industries for nearly a century due to its features, such as simplicity, flexibility, and efficiency. Recently, the control concepts of non-linear processes in the industries are turned towards the attention of the intelligent controllers, such as Genetic Algorithm tuned PI Controllers, Neural Networks tuned PI Controller, Model Predictive Controller, Nonlinear Adaptive Controller, Fuzzy Logic based Controller, Neuro tuned Predictive Controller, etc. This paper focuses on the Investigation of Genetic Algorithm tuned PI Controller for the Nonlinear Conical tank Level Process. The Conical tank is a highly nonlinear process in which the variation in the area of cross section of the process tank level system with change in shape. In the above work, the Genetic Algorithm tuned PI Controller is especially designed for the control of nonlinear conical tank level process for the exact level maintenance. Also, the designed Genetic Algorithm tuned PI Controller is compared with Conventional PI Controller in Servo operation and Regulatory operation.

Keywords

Nonlinear Conical Tank Level Process, Tuning concept of PI Controller, Genetic Algorithm Tuned PI Controller, Conventional PI Controller.

How to Cite this Article?

Arivalahan, R., Balaji, S., and Arasan, T. P. (2019). Investigation of Genetic Algorithm Tuned PI Controller for the Non-Linear Conical Tank Level Process.i-manager's Journal on Instrumentation and Control Engineering, 7(2), 1-8. https://doi.org/10.26634/jic.7.2.16725

References

[1]. Åström, K. J., & Hägglund, T. (2001). The future of PID control. Control Engineering Practice, 9(11), 1163-1175. https://doi.org/10.1016/S0967-0661(01)00062-4

[2]. Bhaba, P. K., Sathishbabu, S., Asokan, A., & Karunanithi, T. (2007). Real time implementation of Wiener Model PI (WMPI) controller in a conical tank liquid level process. Applied Sciences, 7(15), 2194-2197. https://doi.org/10. 3923/jas.2007.2194.2197

[3]. Bhaba, P. K., & Somasundaram, S. (2009). Real time implementation of a new CDM-PI control scheme in a conical tank liquid level maintaining system. CCSE Modern Applied Science, 3(5), 38-45.

[4]. Bhuvaneswari, N. S., Uma, G., & Rangaswamy, T. R. (2008). Neuro based model reference adaptive control of a conical tank level process. Control and Intelligent Systems, 36(1), 98-106. https://doi.org/10.2316/Journal. 201.2008.1.201-1895

[5]. Coon, C., Cohen, G. H. & Coon, G. A. (1953). Theoretical consideration of retarded control. Transactions of the ASME, 75(1), 827-834.

[6]. David, L. (1991). Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.

[7]. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, First Edition. Addison Wesley Publishing Company, USA.

[8]. Holland, J. H. (1992). Genetic Algorithms. Scientific American, 267(1), 66-73.

[9]. Ingimundarson, A., & Hägglund, T. (2002). Performance comparison between PID and dead-time compensating controllers. Journal of Process Control, 12(8), 887-895. https://doi.org/10.1016/S0959- 1524(02)00017-3

[10]. Levine, W. S. (2000). The Control Handbook. FL:CRC press.

[11]. Liptak, B. G. (1995). Process Control, Instrumentation rd Engineers Handbook (3 Ed). UK: Butterworth and Heinemann Company.

[12]. Madhubala, T. K., Boopathy, M., Chandra, J. S., & Radhakrishnan, T. K. (2004, January). Development and tuning of fuzzy controller for a conical level system. In Proceedings of International Conference on Intelligent Sensing and Information Processing, 2004 (pp. 450-455). IEEE. https://doi.org/10.1109/ICISIP.2004.1287699

[13]. Manic, K. S., Sivakumar, R., Nerthiga, V., Akila, R., & Balu, K. (2009). Design of plant estimator model using neural network. IJCSNS, 9(6), 142-147.

[14]. Nithya, S., Gour, A. S., Sivakumaran, N., Radhakrishnan, T. K., Balasubramanian, T., & Anantharaman, N. (2008). Design of intelligent controller for non-linear processes. Asian Journal of Applied Sciences, 1(1), 33-45. https://doi.org/10.3923/ajaps. 2008.33.45

[15]. Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control, 13(4), 291-309. https://doi.org/10.1016/S0959- 1524(02)00062-8

[16]. Valarmathi, K., Devaraj, D., & Radhakrishnan, T. K. (2007). Adaptive enhanced genetic algorithm base proportional integral controller tuning for pH process. Instrumentation Science and Technology, 35(6), 619-635. https://doi.org/10.1080/10739140701651607

[17]. Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Transactions of ASME, 64(11), 759-765.

Investigation of Genetic Algorithm Tuned PI Controller for the Non-Linear Conical Tank Level Process

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content:

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	200	35	35	200	15
Pdf	35	35	200	20
Pdf & Online	35	35	400	25