Top PDF Fuzzy logic control of centralized chilled water system

Fuzzy logic control of centralized chilled water system

Fuzzy logic control of centralized chilled water system

Fuzzy logic controllers are capable of controlling nonlinear process model significantly better than linear controllers [1]. Castro, Castillo and Melin [2] implemented interval type-2 fuzzy controller in truck backer-upper system and compared the results with type-1 fuzzy controller. It shows that both controllers are able to control the car trajectories with similar performance. Birkin and Garibaldi [3] compared the performance of type-1 and type-2 fuzzy logic controllers with PID controller in micro-robot. Results show that both type-1 and type-2 fuzzy controller have similar performance and can perform better than PID controller. However, studies show that not many fuzzy logic based controllers are used in the application of HVAC control [4]. Becker, Oestreich, Hasse and Litz [5] applied fuzzy controller in the refrigeration system to control temperature and relative humidity. Results show that fuzzy controller has better performance when induced with disturbances and change of set point compared to on-off controller. Adaptive fuzzy controller was also successfully implemented in HVAC system to control indoor thermal comfort as in [6]. The controller shows its capability to fast control the indoor comfort conditions even though the outdoor condition varies. Aprea, Mastrullo and Renno [7] have successfully developed fuzzy controller in choosing appropriate compressor speed in refrigeration plant. Soyguder, Karakose and Alli [8] designed self-tuning PID-type fuzzy adaptive control for HVAC system which has two different zones. Most of the previous papers that implemented fuzzy based controller used five-term membership functions or higher as in [3, 5-8]. It is because the higher number of membership functions means the higher rules which results in better accuracy as it reduces the root mean square error [9]. Only a handful of papers
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Analysis of Fuzzy Logic Based Control 
		model for water treatment plant in Indian scenario

Analysis of Fuzzy Logic Based Control model for water treatment plant in Indian scenario

consumption by clustering. Lakhya jyoti phukon et al., (2015) has described Design of fuzzy logic controller for performance optimisation of induction motor using indirect vector control method. Neeru Gupta et al, (2012) have analyzed “ Application of Neural Networks and Fuzzy Logic for Integer water Management. Sirigiri, p. et al ., (2012) was analyzed Evaluation of teacher’s Performance using Fuzzy logic Techniques. And R.Giordano, et al., (2007) has analyzed The Integrating conflict analysis and consensus reaching in a decision support system for water resource management. Rehan Sadiq et al ., (2006) was evaluated the interpreting fuzzy cognitive maps using fuzzy measures to evaluate water quality failures in distribution networks. Vasic Kaninova et al ., (2015) was described The Fuzzy model - based Neural Network Predictive Control of a Heat Exchanger. 3. WATER TREATMENT OVERVIEW
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Design And Realization Of Fuzzy Logic Control For Ebb And Flow Hydroponic System

Design And Realization Of Fuzzy Logic Control For Ebb And Flow Hydroponic System

Abstract: Ebb and flow hydroponic system is one of the hydroponics techniques that work by flowing the growth media with nutrient solution for a certain period time and the unabsorbed nutrient is then fed back to the tank. Normally, this hydroponics system uses a timer for the water filling process which causes inefficient used of nutrient solution. This paper proposes the ebb and flow hydroponics system based on fuzzy logic to control the working of pump in distributing the nutrient solution to the growth media. The control system was implemented using Arduino UNO with temperature sensor and soil moisture sensor as a transducer input and dc motors as actuators channeling nutrients to the planting media. The results confirm that design of fuzzy logic control is able to realize and working properly. There are several operating schemes obtained during testing at temperature of 30 C including: (1) fast-rotating of pump upon reaching moisture of 0.1% RH, (2) medium-rotating of pump at moisture is 30% RH, (3) slow-rotating of pump at moisture of 50% RH, and (4) pump-off at moisture of 74.2% RH. The experimental results have also been validated with Matlab simulation and manual mathematics calculation. The actual testing was performed by growing green bean plants resulting 22 cm height of plants with 14 leaves after 28 days.
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Fuzzy Logic Driven Sliding Mode Controller for Boiler Water Level Control

Fuzzy Logic Driven Sliding Mode Controller for Boiler Water Level Control

A fuzzy logic driven optimized sliding mode control is proposed to control the water level in the boiler drum. The sliding surface is designed by taking error of drum water level, its velocity and its acceleration in to s-function [11]. Then sliding mode parameter η like in the forcing function is optimized by fuzzy logic. The inputs to fuzzy logic are reference input and the system output. The block diagram of the sliding mode control with fuzzy logic for water level control is as shown in Fig. 2.
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ENHANCEMENT OF MICROGRID CONTROL IN DISTRIBUTION SYSTEM USING FUZZY LOGIC TECHNIQUE

ENHANCEMENT OF MICROGRID CONTROL IN DISTRIBUTION SYSTEM USING FUZZY LOGIC TECHNIQUE

593 | P a g e power-electronics converters. However, the grid will become much more complex due to the increasing number of DG systems. For instance, the traditional one way power flow is broken by the bidirectional power flow. The top-down centralized control changes to the bottom up decentralized control. Furthermore, more voltage quality problems may be introduced if the DG systems are not well controlled and organized. It has been implicated that power electronics-based converters not only can service as interfaces with the utility grid, but also have the potential for mitigating power quality problems .Some auxiliary functions such as active filtering have been reported Other works such as voltage unbalance compensation, grid support, and ride-through control under voltage dips have been presented in .Therefore, to adapt to future smart grid application, it will be a tendency of grid interfacing converters to integrate voltage quality enhancement and DG together. This paper focuses on the grid-interfacing architecture, taking into account how to interconnect DG systems in the future grid with enhanced voltage quality The desirable approach should be able to maintain high-quality power transfer between DG systems and the utility grid, even in disturbed grids, and be able to improve the voltage quality at both user and grid side. Figure 1 shows an example of the future application of grid-interfacing converters .
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Fuzzy Logic Space Vector Direct Torque Control of PMSM for Photovoltaic Water Pumping System

Fuzzy Logic Space Vector Direct Torque Control of PMSM for Photovoltaic Water Pumping System

The centrifugal pump applies a load torque proportional to the square of the rotational speed of the motor. Centrifugal pump is the most commonly employed type of pumps [16], it has a relatively high efficiency and capable of pumping a high volume of water. The performances Q, h and P are given in terms of the speed using the following relationships:

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HYDRO ELECTRIC POWER DAM CONTROL SYSTEM USING FUZZY LOGIC

HYDRO ELECTRIC POWER DAM CONTROL SYSTEM USING FUZZY LOGIC

This research presents the construction design of Hydro-Electric Power Dam Control System using Fuzzy Logic. In this design two input parameters: water level and flow rate and three output parameters: release valve control, drain valve control and penstock switching are used. This proposed system uses a simplified algorithmic design approach with wide range of input and output membership functions. The hardware of control system for fuzzifiers and defuzzifiers is designed according to the need of system. The proposed simplified algorithmic design is verified using MATLAB simulation and results are found in agreement to the calculated values according to the Mamdani Model of the Fuzzy Logic Control System.
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Fuzzy Logic Temperature Control System For The Induction Furnace

Fuzzy Logic Temperature Control System For The Induction Furnace

Abstract: This research paper describes the fuzzy logic temperature control system of the induction furnace. Temperature requirement of the heating system varies during the heating process. In the conventional control schemes, the switching losses increase with the change in the load. A closed loop control is required to have a smooth control on the system. In this system, pulse width modulation based power control scheme for the induction heating system is developed using the fuzzy logic controller. The induction furnace requires a good voltage regulation to have efficient response. The controller controls the temperature depending upon weight of meat, water and time. This control system is implemented in hardware system using microcontroller. Here the fuzzy logic controller is designed and simulated in MATLAB to get the desire condition.
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Water Level Monitoring and Control Using Fuzzy Logic System

Water Level Monitoring and Control Using Fuzzy Logic System

Fuzzy logic is a form of knowledge representation appropriate for ideas that cannot be defined exactly, but which depend upon their contexts. It is a means of computing with expressions rather than numbers. It enables computerized devices to reason more like humans, and imitates the capability to reason and use estimated data to find answers [3]. It also permits control engineers to competently build up control strategies in application areas noticeable by low order dynamics with weak nonlinearities. It offers a wholly special approach to solve control problem. This method focuses on what the system should do rather than trying to understand how it works. Fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy. It is a means of controlling with sentences rather than equations. It can be applied for the control of liquid flow and level in any processes [4]. They are known for their ability to provide very good control of a system that is both nonlinear and time varying. Fuzzy logic models interpret the human actions and are also called intelligent systems.
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Design, Manufacturing of Chilled Water System for Process Cooling Application

Design, Manufacturing of Chilled Water System for Process Cooling Application

Our aim is to design a custom built water chiller system. The concept or methodology used in the design of water chiller. In vapor compression system there are four major components: evaporator, compressor, condenser and expansion device. Power is supplied to the compressor and heat is added to the system in the evaporator, whereas in the condenser heat rejection occurs. Heat rejection and heat addition are dissimilar to different refrigerants. A standard vapor compression cycle consists of four processes reversible adiabatic compression from the saturated vapor to the compressor pressure followed by a reversible heat rejection at constant pressure causing de-superheating and condensation. This is further extended to an irreversible expansion at constant enthalpy from saturated liquid to evaporator pressure and there after a reversible heat addition at constant pressure causing evaporation to saturated vapor. The main advantages of this design system are that the flow of water can be controlled.
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Study on the Elman Neural Network Operation Control Strategy of the Central Air Conditioning Chilled Water System*

Study on the Elman Neural Network Operation Control Strategy of the Central Air Conditioning Chilled Water System*

For the operation control of the central air conditioning water system, generally control the temperature difference and pressure difference. More scholars have done a lot of experimental research and engineering verification on the control method for temperature difference and pressure difference [1]. K. F. Fong opti- mization temperature setpoint of chilled water by EP genetic algorithm, water pump control strategy of air conditioning water system are analyzed and expe- rimental by Brian J. Moore and Jamess B. [2] [3]. For the control strategy and
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Designing and simulation a motion control system of mobile robot based on fuzzy logic

Designing and simulation a motion control system of mobile robot based on fuzzy logic

The main task of motion control of mobile robot is to achieve the goal while avoiding obstacles. For this purpose we propose to use some multiple motion modes, namely, “to the target”, “obstacle avoidance”, “along the right wall” and “along the left wall”. The implementation of each motion mode is carried out by using a method based on fuzzy logic. Using multiple motion modes provides the possibility of parallel computation values of various motion modes and the ability to easy adapt a mobile robot to perform other tasks which are carried out by adding extra modes that implemented the required behavior. For motion modes “obstacle avoidance”, “along the right wall” and “along the left wall” the input linguistic variables are distances to obstacles as measured from the front side, the right side and the left side of a mobile robot. For motion mode “to the target” the input linguistic variable is an angle error that is calculated as the difference between the desired heading required to reach the goal and the actual current heading. The output linguistic variable of each motion mode is rotation angle of a mobile robot. The input signals are received from sensors, which are placed in the front, right and left sides of the mobile robot and a machine vision system. The main problem of using multiple motion modes is to obtain the resulting rotation angle of mobile robot, because the different output data can be received from different motion modes. That is necessary to determine the most effective rotation angle of mobile robot using the obtained rotation angles of different motion modes. We propose to calculate some coefficients which express the degree of activation of each motion mode for the determination of the effective rotation angle of mobile robot. Calculating the activation coefficients is carried out by using method based
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Fuzzy Logic Based CSTR Control

Fuzzy Logic Based CSTR Control

Conventional controllers now can be replaced with intelligent controllers like fuzzy logic controller which generate fast dynamic response. Compared to conventional controller’s fuzzy logic controllers are better in complex problem solving. A fuzzy logic controller mainly consist of three section namely fuzzifier, inference engine and defuzzifier.

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Operation and control of a Fuzzy Logic control based Wind power Generation System with Microgrid

Operation and control of a Fuzzy Logic control based Wind power Generation System with Microgrid

flowing streams between the inverters, the inverter yield voltages of inverters 1 also, 2 are directed to a similar voltage. Through the EMS, the yield voltages of inverters 1 and 2 are consistently observed to guarantee that the inverters keep up similar yield voltages. The concentrated EMS is additionally in charge of different parts of control administration, for example, stack anticipating, unit responsibility, monetary dispatch and ideal power stream. Critical data for example, field estimations from keen meters, transformer tap positions and electrical switch status are altogether sent to the brought together server for preparing through wireline/remote correspondence. Amid typical operation, the two inverters will share the most extreme yield from the PMSGs (i.e., every inverter shares 20 kW). The most extreme power created by each WT is evaluated from the ideal breeze control Pwt, Opt as takes after [23]:
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PID Fuzzy Logic Controller System for DC Motor Speed Control

PID Fuzzy Logic Controller System for DC Motor Speed Control

Suatu sistem kendali yang baik harus mempunyai ketahanan terhadap disturbance dan mempunyai respon yang cepat dan akurat. Sering terjadi permasalahan dalam sistem kendali Proportional Integral Derivative (PID) bila dibuat sangat sensitif, maka respon sistem terhadap disturbance menghasilkan overshot/undershot yang besar sehingga kemungkinan dapat terjadi osilasi semakin tinggi. Bila dibuat kurang sensitif memang akan menghasilkan overshot/undershot kecil, tetapi akibatnya akan memperpanjang recovery time. Untuk mengatasi hal ini, diterapkan sistem kendali hybrid yaitu sistem kendali PID yang akan dihybridkan dengan sistem kendali logika fuzzy. Dalam sistem ini kendali utama adalah kendali PID sedangkan kendali logika fuzzy bekerja membantu untuk meminimalkan overshot/undershot yang terjadi dan juga meminimalkan recovery time dari respon sistem. Sistem kendali logika fuzzy yang didesain mempunyai 2 input yaitu error dan delta error dan output kecepatan motor. Besar output dari sistem kendali logika fuzzy hanya 50 % dari kendali PID. Hal ini dilakukan dengan membatasi semesta pembicaraan dari himpunan fuzzy untuk output. Dari desain sistem ini diharapkan sistem kendali secara keseluruhan yang merupakan hybrid antara PID dengan Kendali Logika Fuzzy dapat menghasilkan respon sistem yang lebih baik.
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Development of a boost convertor for photovoltaic system MPPT using fuzzy logic control

Development of a boost convertor for photovoltaic system MPPT using fuzzy logic control

Fuzzification is a process of making a crisp quantity fuzzy. Before this process is taken in action, the definition of the linguistic variables and terms is needed. Linguistic variables are the input or output variables of the system whose values are words or sentences from a natural language, instead of numerical values. A linguistic variable is generally decomposed into asset of linguistic terms. Example, in the air conditioner system, Temperature (T) is linguistic variable represents the temperature of a room. To qualify the temperature, terms such as ―hot‖ and ―cold‖ are used in real life. Then, Temperature (T) = {too cold, cold, warm, hot, too hot} can be the set of decomposition for the linguistic variable temperature. Each member of this decomposition is called a linguistic term and can cover a portion of the overall values of the temperature. To map the non-fuzzy input or crisp input data to fuzzy linguistic terms, membership functions is used.
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Transient Stability Improvement in Transmission System Using SVC with fuzzy logic Control

Transient Stability Improvement in Transmission System Using SVC with fuzzy logic Control

In power system the transmission line are becoming more advanced and stressed due the increase in load. The new technology which is known as Flexible AC transmission system (FACTS) devices, are found very extensively to reduce this stress without disturbing the desired stability margin. In this paper, a fuzzy logic based supplementary controller for Static Var Compensator (SVC) [1]. Flexible AC Transmission System (FACTS) controllers, such as Static VAR Compensator (SVC) and Static Synchronous Compensator use the latest technology of power electronic switching devices in electric power transmission systems to control voltage and power flow. The stability enhancement can be done by using FACTS controllers [2]. In this paper, a fuzzy logic based supplementary controller for Static Var Compensator (SVC) is developed which is used for improve the power quality of generated and distributed power [3]. The designed model has been tested in a 2 machine 3 bus test system using MATLAB software. The simulation results show that the new controller logic gives better and quick performance compared to the other types [4].
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Centralized Wireless Home Appliances Control System

Centralized Wireless Home Appliances Control System

There exists system neither at cheaper rates nor easy to handle. Various systems are hard to install, difficult to use and maintain. Current systems are generally proprietary, closed and not very user friendly. All the existing systems are energy savings, convenient and secure but also hard to install and implement which increases the complexity. Also, not all the systems are compatible with one another and their cost is higher to afford [4] . Most advanced home automation systems in

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Indoor Air Quality Monitoring System With Fuzzy Logic Control Based On IOT

Indoor Air Quality Monitoring System With Fuzzy Logic Control Based On IOT

Polluted air in indoor environment can be contaminated by harmful chemicals and others materials [1]. Air pollution can lead to various diseases such as asthma, wet lung, even coronary heart. Pollution can be done outside or indoors. However, people spend around 90% of their activities indoor, such as at office, homes, school, etc. [2]. CO2 is one of highest elements in indoor environment due to respiration and activities of human inside the room. High level of CO2 can make variety of irritants and decrease cognitive performance [3]. Another material in the air that can effect for human health is PM10. There is standard for indoor air quality gases concentration in room from ASHRAE (American Society of Heating, Refrigerating, and Air-Conditioning Engineers). For PM10, Based on US Environmental Protection Agencies (EPA), the standards for PM10 concentration in 24-hour is 150 ug/m 3 . For carbon dioxide ppm maximum in indoor room that still make comfort for human odor in room, ASHRAE has standard 1000 PPM maximum in the room [4]. Air quality has index that represent the quality of air. That has value from 0 to 500 called AQI. Indoor air quality monitoring needs to be implemented to control air quality in room. Indoor air quality monitoring can ensures that indoor environment in room is safe for stay or do activities[5]. With the current technology, Indoor air quality monitoring can also integrated with Internet .Internet of things concept can be implement on system indoor air quality monitoring. Internet of Things is technology that can make something smarter than before [6].
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MODIFICATION OF FUZZY LOGIC RULE BASE IN THE OPTIMIZATION OF TRAFFIC LIGHT CONTROL SYSTEM

MODIFICATION OF FUZZY LOGIC RULE BASE IN THE OPTIMIZATION OF TRAFFIC LIGHT CONTROL SYSTEM

Blokpoel and Vreeswijk (2016) reported that the queue length is the most important parameter for each traffic light control system to make informed decisions. The work uses the concept of cooperative awareness message to transmit the needed information. In the cooperative approaches, three algorithms were employed, the first being the Global Positioning System (GPS) used to gather data of oncoming vehicles. However, this approach is saddled with the problems of atmospheric disturbances that distort signals before they reach the receiver, reflections from buildings and other large, solid objects can affect the accuracy of the GPS including the time keeping accuracy (Peter, Tella & Gabriel, 2015). The second algorithm uses a model of the wave speed of accelerating vehicles. The speed of a wave can be altered by alterations in the properties of the medium through which it travels. This too can affect the data collected for decision making. The third algorithm concentrates on lower penetration and uses both the classical finish line recognition with cooperative recognition to calculate the queue length.
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