Fuzzy Control of Induction Motor Actuator with Open Loop PID Controller in Water Treatment Plant
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Abstract
To solve the problems in the water plant crisis, intelligent control is a highly potent technique. A fuzzy PID controller with auto-tuner capabilities is used to control the actuator in the power plant circuit. The frequency converter's controller is used with an induction motor (IM) as the actuator to stabilize the water flow across a predetermined distance. Simscape/Matlab 2020a was used to simulate the water plant (WP) circuit with the tank station in the water supply system before it was tested over various distances to gauge its current, speed, torque, and voltage. In the present model, four pump stations were used. The pressures were estimated at different distances, i.e., 40 m, 45 m, and 60 m, from the water treatment plant. In the fuzzy logic controller with 7x7 rules, the Mamdani inference engine was used. All electrical parameters were constant, while the pressure validated the system's stability. The findings demonstrated the circuit’s effectiveness and constant pressure within the water treatment circuit.
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Mahmmoud ON, Mehdi SR, Gaeid KS, Al-Tameemi ALS. Solar Cell Split Source Inverter for Induction Motor with Computer Control. International Journal of Power Electronics and Drive Systems 2023; 14(1): 174-184. DOI: https://doi.org/10.11591/ijpeds.v14.i1.pp174-184
Gaeid KS, Homod RZ, Al Mashhadany Y, Al Smadi T, Ahmed MS, Abbas AE. Describing Function Approach with PID Controller to Reduce Nonlinear Action. International Journal of Electrical and Electronics Research 2022; 10(4):976-983. DOI: https://doi.org/10.37391/ijeer.100437
Guven AH, Ersahin ME, Ozgun AB, Ozturk I, Koyuncu I. Energy and Material Refineries of Future: Wastewater Treatment Plants. Journal of Environmental Management Volume 2023; 329: 117130, (1-6). DOI: https://doi.org/10.1016/j.jenvman.2022.117130
Salih RA, Ghdhban II, Wahid AK. Evaluation of a Number of Water Treatment Plants in Kirkuk Governorate Using the Water Quality Index (WQI). Tikrit Journal of Engineering Sciences 2018; 25(1): 49–59. DOI: https://doi.org/10.25130/tjes.25.1.08
Jassim AH, Hussein AA, Abbas LF. The Performance of a Three-Phase Induction Motor under and Over Unbalance Voltage. Tikrit Journal of Engineering Sciences 2022; 28(2): 15–32. DOI: https://doi.org/10.25130/tjes.28.2.02
Baskys A. Switched-Delay Smith Predictor for the Control of Plants with Response-Delay Asymmetry. Sensors 2023; 23(1): 258, (1-17). DOI: https://doi.org/10.3390/s23010258
Larroca FP, Olschewski ES, Quino-Favero J, Huamaní JR, Castillo Sequera JL. Water Treatment Plant Prototype with pH Control Modeled on Fuzzy Logic for Removing Arsenic Using Fe(VI) and Fe(III). Water 2020: 12: 2834, (1-13). DOI: https://doi.org/10.3390/w12102834
Kaleeswari K, Johnson T, Johnson C, Vijayalakshmi C. Analysis of Fuzzy Logic Based Control Model for Water Treatment Plant in Indian Scenario. International Journal of Pure and Applied Mathematics 2018; 119(9): 11-20.
Chakravarty SP, Roy A, Roy P. Control of Activated Sludge Treatment Process Using Pre-Compensated Multi-Variable Quantitative Feedback Theory - Based Controller. Transactions of the Institute of Measurement and Control 2022; 44(2): 506-522. DOI: https://doi.org/10.1177/01423312211039048
Cao W, Yang Q. Online Sequential Extreme Learning Machine Based Adaptive Control for Wastewater Treatment Plant. Neurocomputing 2020; 408(1): 169–175. DOI: https://doi.org/10.1016/j.neucom.2019.05.109
Quevedo J, Chen H, Cugueró MÀ, Tino P, Puig V, Garciá D, Yao X. Combining Learning in Model Space Fault Diagnosis with Data Validation / Reconstruction: Application to the Barcelona Water Network. Engineering Applications of Artificial Intelligence 2014; 30: 18–29. DOI: https://doi.org/10.1016/j.engappai.2014.01.008
Flores TKS, Villanueva JMM, Gomes HP, Catunda SYC. Indirect Feedback Measurement of Flow in a Water Pumping Network Employing Artificial Intelligence. Sensors 2021; 21(1):75, (1-15). DOI: https://doi.org/10.3390/s21010075
Suraj G, Pabitra KB, Belqasem A, Sudhakar BT, Sajal KD. Modelling, Simulation and Performance Comparison of different Membership Functions Based Fuzzy Logic Control for an Active Magnetic Bearing System. The Journal of Engineering 2023; e12229:1-20. DOI: https://doi.org/10.1049/tje2.12229
Ren GP. Design of Interval type-2 Fuzzy Controllers for Active Magnetic Bearing Systems. IEEE/ASME Transactions on Mechatronics 2020; 25(5):2449–2459. DOI: https://doi.org/10.1109/TMECH.2020.2978018
Carvalho CF, Oliveira FD, Molina LF, Cavalini AAJ, Steffen V. Fuzzy Robust Control Applied to Rotor Supported by Active Magnetic Bearing. Journal of Vibration and Control 2021; 27(7–8): 912–923. DOI: https://doi.org/10.1177/1077546320933734
Lund NSV, Falk AKV, Borup M, Madsen H, Steen MP. Model Predictive Control of Urban Drainage Systems: A Review and Perspective Towards Smart Real-Time Water Management. Critical Reviews in Environmental Science and Technology 2018;48(3): 279–339. DOI: https://doi.org/10.1080/10643389.2018.1455484
Pedersen EB, Herbertsson HR, Niemann H, Poulsen NK, Falk AKV. Model Predictive Control of Sewer Networks. 13th European Workshop on Advanced Control and Diagnosis (ACD 2016) 17–18 November 2016; Lille, France: p. 1-12.
Ochoa D, Riaño-Briceño G, Quijano N, Martinez OC. Control of Urban Drainage Systems: Optimal Flow Control and Deep Learning in Action. American Control Conference (ACC) 2019; Philadelphia, PA, USA. IEEE: p. 1-6. DOI: https://doi.org/10.23919/ACC.2019.8814958
Camboim MM, Villanueva MM, Souza DP. Fuzzy Controller Applied to a Remote Energy Harvesting Emulation Platform. Sensors 2020; 20; 5874, (1-18). DOI: https://doi.org/10.3390/s20205874
Lee SW. Practical Feedback Loop Analysis for Voltage-Mode Boost Converter. Application Report No. SLVA057. Texas Instruments 2014.