Design and Implementation of Intelligent Controllers for Pressure Process Station
P. Vaishnavi1, K. Sneha2, G. Sathish Kumar3, P. Kirubashankar4
1Vaishnavi P*, Assistant Professor, Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India.
2Sneha K, Assistant Professor, Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India.
3Sathish Kumar G, Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India.
4Kirubashankar P., Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India.
Manuscript received on March 05, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on April 30, 2020. | PP: 622-626 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7677049420/2020©BEIESP | DOI: 10.35940/ijeat.D7677.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Pressure Measurement is very essential parameter and an important one in pressure station. Excessive Pressure needs to be controlled before it affects the pressure station in any immediate situation. Pressure is one the parameter which is a basic key requirement for many industrial as well as control system applications such as industrial HVAC, Coating (CVD/PVD), Food and beverage industries, water treatment and etc., There are three main types of pressure measuring devices which are framed using these three pressure types are absolute, gauge and differential. The pressure transmitter plays an instrumental part while dealing with the pressure measurement in the process station. In this application, an intelligent network controller has been developed to minimize the settling error which is enveloped during the process. And also a neural network predictive controller, Fuzzy logic controller was drawn up for the operation and compared with ZN tuning PID controller. And these controller results are compared the standard error measurement. The errors of ISE, IAE and IATE are used for the primary comparison of the results acquired in these controllers.
Keywords: Fuzzy Logic Controller (FLC), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Square Error (ISE), Neural Network predictive Controller, Ziegler Nichols (Z-N).