Self-Adaptive Fuzzy PID Controller for Water Supply System

Ding, Chengsong; Cao, Lijun

doi:10.1109/icmtma.2010.421

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…In [ 11 , 12 , 13 ], the authors introduced pressure control strategies using current automation tools, such as the use of a programmable logic controller (PLC) and frequency converters as well as fuzzy and fuzzy-PID control strategies, ensuring pressure stability in the supply network. In this work, dynamic response comparisons were performed, showing that the fuzzy-PID controller presented fast responses, with low overshoot and high noise immunity, but it required the construction of a very accurate knowledge base from the specialist.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements

Araújo

Villanueva

Cordula³

et al. 2022

Sensors

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As hydroenergetic losses are inherent to water supply systems, they are a frequent issue which water utilities deal with every day. The control of network pressure is essential to reducing these losses, providing a quality supply to consumers, saving electricity and preserving piping from excess pressure. However, to obtain these benefits, it is necessary to overcome some difficulties such as sensing the pressure of geographically distant consumer units and developing a control logic that is capable of making use of the data from these sensors and, at the same time, a good solution in terms of cost benefit. Therefore, this work has the purpose of developing a pressure monitoring and control system for water supply networks, using the ESP8266 microcontroller to collect data from pressure sensors for the integrated ScadaLTS supervisory system via the REST API. The modeling of the plant was developed using artificial neural networks together with fuzzy pressure control, both designed using the Python language. The proposed method was tested by considering a pumping station and two reference units located in the city of João Pessoa, Brazil, in which there was an excess of pressure in the supply network and low performance from the old controls, during the night period from 12:00 a.m. to 6:00 a.m. The field results estimated 2.9% energy saving in relation to the previous form of control and a guarantee that the pressure in the network was at a healthy level.

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Section: Related Workmentioning

confidence: 99%

Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements

Araújo

Villanueva

Cordula³

et al. 2022

Sensors

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“…Whether the PID control effect is good or bad depends entirely on the PID parameters adjustment. For the disadvantages of traditional PID control, combining fuzzy control with PID control, this paper adopts the parameter self-tuning fuzzy PID control algorithm [3][4][5][6]. PID control is used when the error is small, and fuzzy control is adopted if the error is large, and the two controls are both applied in the sinter mixture moisture.…”

Section: Control Strategymentioning

confidence: 99%

The Sinter Mixture Moisture Control System Based on Fuzzy PID Controller

Gong

2013

AMM

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Because the sinter mixture moisture control system has time-varying, nonlinear and time-delay characteristics, it is difficult to establish its precise mathematical model. According to the defects that the conventional PID control in the fixed parameter is difficult to guarantee the system performance, this paper proposed the parameter self-tuning fuzzy PID controller which is used to the system. Based on the sintering furnace structural features and control performance requirements, the PID parameters are set by fuzzy logic reasoning. The actual operation results proved that the parameter self-tuning fuzzy PID controller improved more obviously the system performance than the original PID controller, and stabilized the sinter mixture moisture, and improved the sintering production condition and promoted the production capacity.

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