Fuzzy Intelligent Controller for the Maximum Power Point Tracking of a Photovoltaic Module at Varying Atmospheric Conditions

This paper presents the modeling of a photovoltaic (PV) module at varying atmospheric conditions such as irradiation and temperature. It also includes the maximum power point tracking (MPPT) of the PV module using conventional perturb and observe (P&O) method and fuzzy logic controller. For the performance analysis, the simulation of the PV module along with MPPT controller is done by using MATLAB/Simulink software. The voltage, current and power transitions at varying irradiation and temperature conditions is observed using conventional P&O and fuzzy logic based MPPT controllers. Finally the percentage improvement in power tracking time by fuzzy logic controller against the P&O controller has been evaluated Keywords: Photovoltaic Module, MPPT, P&O method, Fuzzy logic Controller, Irradiatio

Comparative Study of a Fuzzy Logic Based Controller and a Neuro-Fuzzy logic Based Controller for Computer Fan

The impact of soft-computing in modern day engineering and technology cannot be overemphasized.  Fuzzy logic approach as proposed by Lofti Asker Zadeh, popularized by the Japanese, has found its way into the control of many domestic and industrial appliances/machines.  Unlike the popular PID controllers and the pulse width modulation based controllers, the performance of computer fan is investigated using the fuzzy logic approach with two inputs parameters, that is, the computer loads and the temperature and one output parameter which is the speed at which the computer fan operates.  For the fuzzy inference system, four membership functions are selected for the inputs as well as the output.  Relevant rules are set to determine the operating conditions and boundaries for the controller.  In order to make the controller adaptive, neurofuzzy logic appproach is used with parameters set as the case with fuzzy logic. Training of the controller is carried out and the performance of each controller is presented in three dimensional view and two dimensional surface view with neurofuzzy based controller, in performance, having an edge over the fuzzy logic based controller. Keywords: Anfis, Fuzzy logic, Computer fan, Controller, Performance compariso

Design and Implementation of Temperature Controller for a Vacuum Distiller

This paper proposed design and implementation of temperature controller for a vacuum distiller. The distiller is aimed to provide distillation process of bioethanol in nearly vacuum condition. Due to varying vacuum pressure, temperature have to be controlled by manipulating AC voltage to heating elements. Two arduino based control strategies have been implemented, PID control and Fuzzy Logic control. Control command from the controller was translated to AC drive using TRIAC based dimmer circuit. Experimental results show that fuzzy logic controllers have better performance in controlling temperature of vacuum distille

Design and Implementation of Temperature Controller for a Vacuum Distiller

This paper proposed design and implementation of temperature controller for a vacuum distiller. The distiller is aimed to provide distillation process of bioethanol in nearly vacuum condition. Due to varying vacuum pressure, temperature have to be controlled by manipulating AC voltage to heating elements. Two arduino based control strategies have been implemented, PID control and Fuzzy Logic control. Control command from the controller was translated to AC drive using TRIAC based dimmer circuit. Experimental results show that fuzzy logic controllers have better performance in controlling temperature of vacuum distille

Control of proton exchange membrane fuel cell based on fuzzy logic

This paper presents a control strategy suitable for hydrogen/air proton-exchange membrane fuel cells (PEMFCs), based on the process modeling using fuzzy logic. The control approach is tested using a PEMFC stack consisting of 32 cells with parallel channels. An optimal fuzzy-PI controller is designed to mainly control the hydrogen and air/oxygen mass flows, and auxiliary variables such as the temperature, pressure, humidity of the membrane, and proportion of stoichiometry. The fuzzy logic controller possesses many advantages over the PID controllers, such as a higher performance/cost ratio. It is shown experimentally that the optimal fuzzy-PI controller can improve the voltage and current performance of the system when the load changes

Functions of fuzzy logic based controllers used in smart building

The main aim of this study is to support design and development processes of advanced fuzzy-logic-based controller for smart buildings e.g., heating, ventilation and air conditioning, heating, ventilation and air conditioning (HVAC) and indoor lighting control systems. Moreover, the proposed methodology can be used to assess systems energy and environmental performances, also compare energy usages of fuzzy control systems with the performances of conventional on/off and proportional integral derivative controller (PID). The main objective and purpose of using fuzzy-logic-based model and control is to precisely control indoor thermal comfort e.g., temperature, humidity, air quality, air velocity, thermal comfort, and energy balance. Moreover, this article present and highlight mathematical models of indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a fuzzy algorithm

Comparison between PID and Fuzzy Controller to Hydroponic Temperature

Nowadays, there are many researches about automatic controlling system of hydroponic temperature. The two most well-known controller systems are PID Controller and Fuzzy Logic Controller. This study discusses and compares the performance of these two main controller systems to control the room temperature of a wick-based hydroponic system for cherry tomato cultivation. In this comparison system, we use an SHT11 module as the sensor and an air conditioner as the actuator. From the test result, PID controller has 2.2 times longer rise time, whereas Fuzzy controller yields 25.3 times larger overshoot in day time measurement

Utilization of fuzzy controller for laboratory scale convective fruit dryers

In the present study, a fruit dryer system that is controlled based on fuzzy logic is presented. A laboratory scale cabinet was developed which includes four sensors in different lengths for monitoring the cabin temperature and humidity. Fuzzy base controller is a new monitoring technique in food industrial machines that utilize sensors captured values as its input parameters to make a suitable decision according to temperature values. Furthermore, to implement the fuzzy system, a microcontroller base monitoring system is developed. Microcontroller captured temperature samples and converted them in to digital values. Output of the fuzzy controller will control the speed of the fan and power of the heater. Several performed results indicated the amenability of the proposed monitoring system as a drying machine main controller in different drying curves. Fluctuation of the cabin temperature with fuzzy control was smoother than non-fuzzy control. Nevertheless, fuzzy control has a significant influence on the power consumption as well

FUZZY LOGIC UNTUK KONTROL MODUL PROSES KONTROL DAN TRANSDUSER TIPE DL2314 BERBASIS PLC

Process Control and Transducer Module DL2314 type is a De Lorenzo manufactured module that made for control process training that are one set sensor and actuator for the level (height), pressure, temperature and flow. In this module also included a controller module that for controlled actuator based on sensor. This controller module has PID controller which can be used to do some experiments that using sensors in this modul DL2314. This Final Project was used PLC to replace function of control from controller module in DL2314 module, especially on level sensor. PLC that used in this Final Project is PLC Omron CS1H, while the control method that used is fuzzy Logic and PID. The Implementation of Fuzzy Logic in this PLC has a better respond than PID. It looked from the data test result. PID use a controller module DL2314 or PID(190) function on ladder diagram. Although that was error made was bigger than both of them, this fuzzy controller has a high stability level and fast time respond to reach setpoint. In this Final Project it could be resumed that the implementation of fuzzy logic and method and PID can be implemented on PLC, especially PLC Omron CS1H. For PID using PID (190) function on ladder diagram and Fuzzy Logic also using the combination of ladder diagram