TY - GEN
T1 - PLC-Based Fuzzy Logic Controller for Flow Rate Control in Water Pipelines
AU - Adityapriatama, Jeffry
AU - Wijaya, Sastra Kusuma
AU - Prajitno, Prawito
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Soft Computing is a form of computing that is based on data or information that is not or less accurate (imperfect), or that contains uncertainty. The data generated by the sensor is one example of physical quantity information that is less accurate or always contains uncertainty. Therefore, data processing based on soft computing for sensor measurement results is the right choice, which is able to handle the uncertainties in order to produce information, conclusions or decisions that are relatively accurate. In this research study, one type of soft computing, that is fuzzy logic, was applied to design a flow-rate controller. It was expected that by implementing fuzzy logic in the controller, it can handle inaccurate flowmeter sensor readings, therefore a reliable controller can be designed even it uses a low-cost inaccurate flowmeter. Fuzzy logic in this controller uses 2 fuzzy sets, namely error and change of error. Each fuzzy set has 5 membership functions, namely large negative (NB), negative medium (NM), zero (ZO), positive medium (PM) and large positive (PB). This fuzzy system is implemented in a personal computer (PC) that functions as the center of controller that retrieves data from the OLE for Process Control (OPC) server, while the data is actually taken from PLC that is directly connected to the plant. The PC communicates with the PLC using ethernet communication. PC is involved in this design because of the limitations of PLC that cannot be programmed using common programming languages, such as MATLAB. The developed fuzzy logic-based controller is operated on a lab-scale prototype plant, and the analysis of performance is verified experimentally, and monitored using MATLAB SIMULINK. Based on the experimental results it can be concluded that the fuzzy logic-based controller is better than the conventional PID controller. The results show that the fuzzy logic controller is faster to reach steady-state which is 24.42 seconds without overshoot and has a lower root-mean-square error (rmse) of 0.69 compared to the PID controller which is 48.6 seconds with an overshoot of 16.2% and has RMSE about 3.58.
AB - Soft Computing is a form of computing that is based on data or information that is not or less accurate (imperfect), or that contains uncertainty. The data generated by the sensor is one example of physical quantity information that is less accurate or always contains uncertainty. Therefore, data processing based on soft computing for sensor measurement results is the right choice, which is able to handle the uncertainties in order to produce information, conclusions or decisions that are relatively accurate. In this research study, one type of soft computing, that is fuzzy logic, was applied to design a flow-rate controller. It was expected that by implementing fuzzy logic in the controller, it can handle inaccurate flowmeter sensor readings, therefore a reliable controller can be designed even it uses a low-cost inaccurate flowmeter. Fuzzy logic in this controller uses 2 fuzzy sets, namely error and change of error. Each fuzzy set has 5 membership functions, namely large negative (NB), negative medium (NM), zero (ZO), positive medium (PM) and large positive (PB). This fuzzy system is implemented in a personal computer (PC) that functions as the center of controller that retrieves data from the OLE for Process Control (OPC) server, while the data is actually taken from PLC that is directly connected to the plant. The PC communicates with the PLC using ethernet communication. PC is involved in this design because of the limitations of PLC that cannot be programmed using common programming languages, such as MATLAB. The developed fuzzy logic-based controller is operated on a lab-scale prototype plant, and the analysis of performance is verified experimentally, and monitored using MATLAB SIMULINK. Based on the experimental results it can be concluded that the fuzzy logic-based controller is better than the conventional PID controller. The results show that the fuzzy logic controller is faster to reach steady-state which is 24.42 seconds without overshoot and has a lower root-mean-square error (rmse) of 0.69 compared to the PID controller which is 48.6 seconds with an overshoot of 16.2% and has RMSE about 3.58.
KW - Fuzzy Logic Control
KW - MATLAB
KW - OPC
KW - PLC
UR - http://www.scopus.com/inward/record.url?scp=85084755171&partnerID=8YFLogxK
U2 - 10.1109/ICEEIE47180.2019.8981464
DO - 10.1109/ICEEIE47180.2019.8981464
M3 - Conference contribution
AN - SCOPUS:85084755171
T3 - ICEEIE 2019 - International Conference on Electrical, Electronics and Information Engineering: Emerging Innovative Technology for Sustainable Future
SP - 79
EP - 84
BT - ICEEIE 2019 - International Conference on Electrical, Electronics and Information Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Electrical, Electronics and Information Engineering, ICEEIE 2019
Y2 - 3 October 2019 through 4 October 2019
ER -
