Performance of Controllers for Non Linear Process

International Journal of Electrical and Electronics Engineering
© 2019 by SSRG - IJEEE Journal
Volume 6 Issue 11
Year of Publication : 2019
Authors : Mrs. A. Durgadevi, Dr. K.Dhayalini, Mr. R.Saran Raj
: 10.14445/23488379/IJEEE-V6I11P101
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Citation:
MLA Style:

Mrs. A. Durgadevi, Dr. K.Dhayalini, Mr. R.Saran Raj, "Performance of Controllers for Non Linear Process" SSRG International Journal of Electrical and Electronics Engineering 6.11 (2019): 1-6.

APA Style:

Mrs. A. Durgadevi, Dr. K.Dhayalini, Mr. R.Saran Raj,(2019). Performance of Controllers for Non Linear Process. SSRG International Journal of Electrical and Electronics Engineering 6(11), 1-6.

Abstract:

Non-linear process control is a complex task in process industries. Due to the persistently changing cross section and non-linearity of the tank a spherical tank provides a demanding problem for the level control. In this paper the model of a spherical tank system is derived as First Order Plus Dead Time (FOPDT) from the open loop response of real time setup of the system using Lab VIEW software. The intention of this project is to preserve the level inside the process tank at preferred value. The Proportional- Integral (PI) controller is designed to direct the level of the water in spherical tank. But for a non-linear system the same PI controller will give different responses at different operating regions. Hence there is a need for non-linear controller (Model Predictive Controller- MPC) to work in this non-linear region. But the complex control problem has led to use Neural Network (NN) in MPC. The advantage of Neural Network Predictive Controller (NNPC) is that an precise depiction of the process can be obtained by training the NN. The controllers are designed and the performances of these controllers (PI controller, MPC and NNPC) are compared for set point tracking and disturbance rejection using MATLAB. From the results it is inferred that NNPC gives minimum error and better tracking performance

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Key Words:

Model Predictive Controller, Neural Network Predictive Controller, Proportional – Integral controller, Spherical tank.