Mamdani Fuzzy Logic Controller Research Articles

Open circuit fault diagnosis and fault classification in multi-level inverter using fuzzy inference system

Multi-level inverters (MLIs) have been successfully used to integrated the renewable energy sources (RES) into microgrids. However, the operation of MLI is affected when an open circuit fault (OCF) or a short circuit fault occurs. Among these kinds of faults, there is a high prevalence of open circuit faults in MLI. Any fault in MLI must be identified and classified as soon as possible to maintain the reliability of the power supply. This work is focused on developing a Fuzzy Inference System (FIS) for detecting and classifying the open circuit faults in Cascaded H-Bridge Multi-Level Inverter (CHMLI), thereby improving the fault diagnosis accuracy and efficiency. In CHMLI, the gate pulse is generated by pulse width modulation (PWM) technique. The Mamdani Fuzzy Logic Controller (FLC) identifies and categorizes the different OCFs. Fuzzy logic rules are designed for detecting and classifying open circuit faults simultaneously using the fundamental Discrete Fourier components of voltage and current. Several combinations of open circuit faults have been studied in different switches of the MLI, along with the effect of fault inception angle. Furthermore, the test results support the feasibility of the proposed fuzzy-based fault diagnosis and classification scheme in a practical context. A real-time simulation obtained with the help of FPGA-based OPAL-RT 4510 demonstrates the robustness and effectiveness of the designed topology. All types and fault locations are considered in multiple cases of switch failure.

Robotic Arm Position Control using Mamdani Fuzzy Logic on Arduino Microcontroller

A robotic arm is the most often used robot in manufacturing to perform the same task accurately again and over again in a controlled environment. However, direct positioning control of the robotic arm is always inaccurate as it does not consider its position in the external environment. Therefore, this work implements the Mamdani fuzzy logic in the position control of the 6 DOF robotic arm to improve the accuracy and movement of each joint at this manipulator. The implemented fuzzy logic Mamdani inference system is done in MATLAB and finally converted to the C language to accommodate the main microcontroller, Arduino UNO environment. There are 6 servo motors controlled by this approach and the result is compared with the conventional method. It is found that the Mamdani fuzzy logic controller has an average error of 0.67% while the direct control method has an average error of 2.33%. Based on this result, it shows that the use of the Mamdani fuzzy logic increases the accuracy and reduces the swaying movement after stopping the robotic arm movement.

Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller

The substantial rise in the demand for electric vehicles (EVs) has emphasized an environment-friendly and intelligent design for speed control strategies. In this paper, a Mamdani fuzzy logic controller (MFLC) was proposed to vigorously control the speed of EVs at discrete levels. MFLC member functions (MFs) are tuned for EVs operating at three different speed modes (40, 60, and 80 km/h). The proposed speed controller operation for the speed tracking of EVs was designed and tested in MATLAB (Simulink) environment. The proposed speed controller validated a remarkable improvement in dynamic speed control compared with existing P-I, FLC, Fuzzy FOPID (ACO), Fuzzy FOPID (GA), and Fuzzy FOPID (PSO) controllers. Its stability under a user-defined drive pattern is also observed. In this proposed work, the speed controller highlights the better tracking of user-defined speed response compared to the conventional aforementioned controllers. Moreover, the speed tracking of the designed model was tested for robustness against speed transients at predefined time instants, respectively. The comparison suggests that the MFLC model removes overshoot and significantly reduces the steady-state time.

IMPLEMENTASI PENGONTROLAN TEGANGAN KELUARAN DC STEP UP QUADRATIC BOOST CONVERTER DENGAN VARIASI TEGANGAN INPUT DARI SINGLE PV MENGGUNAKAN LOGIKA FUZZY

Resulting output voltage is not constant due to light intensity and surface temperature of the solar panels. To overcome the output voltage of solar panels that tends to fluctuate, is to add a DC-DC converter to the output side of the DC-DC Converter used in this study is the Quadratic Boost Converter type which has a role to increase the output voltage of the monocrystalline type solar panel so that it remains constant at 24V DC. using Mamdani Fuzzy Logic Control as a method of controlling PWM switching.
 The results obtained from this study are that the quadratic boost converter can keep the output voltage of the solar panel constant at 24V, with low ripple voltage and overshoot. The Mamdani fuzzy logic method used can produce a constant output voltage value with a rise time of ± 5 seconds. The efficiency obtained from this converter hardware is quite good, ranging from 76% - 88%.

Компаративна анализа на различни алгоритми за управување на поле од хелиостати

This study presents the use of various algorithms for control of a field of heliostats, through which a thermal power plant with concentrated solar energy is controlled. The design of the control algorithms consists of several steps. First, the Sun tracking alghorithm is presented for a specific location with an accuracy of ± 0.0003°. In order to obtain the mathematical model of the system, the real system is identified according to the gray box and the least-square method. The data used to identify the system is generated by step excitation on the real system, for a specific sampling period. The resulting mathematical model is used to design and simulate a continuous and discrete Proportional-Integral-Derivative (PID) controller, Mamdani and Sugeno fuzzy logic controllers, as well as ANFIS based fuzzy logic controller. The results of the applied controllers are analyzed and compared, based on the output overshoot, the rise and settling time. It can be concluded that we got best results (least settling time and the least overshoot) when fuzzy logic controller with ANFIS was used, while in terms of speed and rise time, the best results were obtained when discrete PID control algorithm was used. The study is extension of the work given in [23].

DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

Kendali pH untuk sistem IoT hidroponik deep flow technique berbasis fuzzy logic controller

In hydroponic cultivation sites, pH control is still carried manually by checking the pH level with a pH meter and providing a pH balancing liquid manually. This study aims to design an automatic pH control system in the Deep Flow Technique (DFT) hydroponic system that uses the Internet of Things (IoT) based Fuzzy Logic Controller (FLC). The SKU SEN0161 sensor detects the pH value as FLC inputs in an error value and its changes. These inputs are processed using Mamdani FLC embedded in the Arduino Mega 2560 microcontroller. The FLC produces an output in a pH liquid feeding duration using the peristaltic pump. The results showed that FLC could maintain the pH value according to the set point with a settling time of less than 50 seconds, both with disturbance by adding pH liquid and without disturbance. The pH value can also be displayed on the website interface system as a monitoring system.

Lux and current analysis on lab-scale smart grid system using Mamdani fuzzy logic controller

The increasing need for electrical energy requires suppliers to innovate in developing electric distribution systems that are better in terms of quality and affordability. In its development, it is necessary to have a control that can combine the electricity network from renewable energy and the main network through voltage back-up or synchronization automatically. The purpose of this research is to create an innovative lux and current analysis on a lab-scale smart grid system using a fuzzy logic controller to control the main network, solar panel network and generator network to supply each other with lab-scale electrical energy. In the control, Mamdani fuzzy logic controller method is used as the basis for determining the smart grid system control problem solving by adjusting the current conditions on the main network and the light intensity conditions on the LDR sensor. Current conditions are classified in three conditions namely safe, warning, and trip. Meanwhile, the light intensity conditions are classified into three conditions namely dark, cloudy and bright. From the test results, the utility grid (PLN) is at active conditions when the load current is 0.4 A (safe) and light intensity is 1,167 Lux (dark). Then the PLN + PV condition is active when the load current is 1.37 (warning) and the light intensity is 8,680 lux (bright). Finally, the generator condition is active when the load current is 1.6 (trip) and the light intensity is 8,680 (bright). Based on the test results, it is known that the system can work to determine which source is more efficient based on the parameters obtained.

Design, Control, and Simulation of a Neonatal Incubator.

In this project, a fully functional incubator with precise control with respect to temperature, humidity, and airflow was developed and assessed. In parallel with the development of the incubator, a heuristic simulation was created to test and tune the Mamdani fuzzy logic controller. The controller was then applied to the incubator prototype.

Comparative Assessment of Control Strategies Used in a Hybrid AC-DC Micro-Grid Consisting of Composite Energy Sources

In the present paper, comparative assessment of two important available intelligent control strategies namely the Mamdani fuzzy logic controller and the T–S fuzzy controller used for DC-link voltage control in DC micro-grid is carried out, first. Then, a new inverter control technique is proposed to maintain the inverter voltage as well as the DC-link voltage in a hybrid AC-DC micro-grid. The DC micro-grid under study comprises of photovoltaic system, permanent magnet synchronous generator-based wind energy system, battery energy storage system, fuel cell, electrolyzer and DC loads. The complete DC Micro-grid along with its correlated control algorithms are examined and accessed through MATLAB/Simulink environment.

Effectiveness of fuzzy sliding mode control boundary layer based on uncertainty and disturbance compensator on suspension active magnetic bearing system

This paper presents the disturbance and uncertainty suppression by using the nonlinear disturbance observer and an extended state observer for a nonlinear active magnetic bearing system. Otherwise, the chattering free is assured by a fuzzy controller, where the fixed sliding mode surface boundary is regulated by fuzzy boundary layer. The stability of the system is guaranteed by Lyapunov condition. First, the nonlinear disturbance observer is presented to estimate the disturbance from outside of the system. Second, the system parameter variations are estimated by an extended state observer with the construction via the estimated disturbance value. Third, the proportional–integral–derivative sliding mode surface has been constructed due to the chattering values that appear from the high-frequency switching control values. Fourth, these chattering values are reduced by using a Mamdani fuzzy logic control. The proposed control methodology was given by the MATLAB simulation. The overshoot value that is equal to zero, narrow settling time, and the average distance tracking error value which is quite small are archived.

Smart grid photovoltaic system pilot scale using sunlight intensity and state of charge (SoC) battery based on Mamdani fuzzy logic control

The Utilization of renewable energy such as a photovoltaic system is the foremost alternative in transfers generated by conventional power plants, but the lack of photovoltaics is support for light intensity. The purpose of this research is to develop a pilot-scale smart grid photovoltaic system that can regulate the supply of electrical energy from either the battery or the power supply. The control system in this study uses the Mamdani fuzzy logic method in determining automatic system performance. This system monitors the intensity of light and battery which are then used as automatic safety parameters on the power supply, battery, and photovoltaic. The results of this study display the indicator results from the microcontroller in supplying electrical energy for the use of electrical loads, Power Supply has been served the load when the battery is in a low state which have a voltage `11 Volts, the battery has been served the load when the condition of the battery is in a medium and high condition which has a voltage of 11.5 `; ....; `13 Volts. PV has been served batteries or loads when the light intensity is cloudy and bright which have a light intensity of 3585 `; ...; `10752 Lux. This system can reduce dependence on conventional energy without reducing the quality of the energy supply at load and Photovoltaic system dependence on light intensity does not affect the supply of energy consumption to electrical loads.

Fuzzy Based Fault Classifier and Time Domain Reflectometry Based Fault Locator for Underground Cables

Cables are insulating wires used for transferring power or transmitting signals. If any fault occurs in the cable then, it causes detrimental effects to power system equipments. The technical difficulty is to find, analyze, and classify faults. In case of underground cables it will be a time consuming and expensive process to repair the faults or replace the cables. This paper aims to analyse the faults by creating the various types of short and open circuit faults, classify them by taking in account of grounding, and to locate the distance at which the fault is occurred. The underground cable is simulated using MATLAB/ Simulink, the faults are created and the Fourier transform of voltage and current are observed at the source end. The generated fault phase current data from the simulation has been normalized using fault index is the input to the Mamdani fuzzy logic controller. The defuzzified output is verified for different phase current values which classify the nature of the fault. Two fuzzy classifiers like ground and phase faults are developed which holds good for different range of cables. A methodology to find the fault location in long transmission line is attempted using PSPICE, by creating short circuit fault. The distance is determined by using velocity of propagation of travelling waves, which is realized using impulse voltage wave generator which is based on impedance

An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

Intelligent mobile robot navigation using a neuro-fuzzy approach

This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.

Intelligent mobile robot navigation using a neuro-fuzzy approach

This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.

Control Strategies for the Grid Side Converter in a Wind Generation System Based on a Fuzzy Approach

Abstract Two techniques for the control of a grid side converter in a wind energy conversion system. The system is composed of a fixed pitch angle wind turbine followed by a permanent magnet synchronous generator and power electronic converters AC-DC-AC. The main interest is in how to control the inverter in order to ensure the stability of the DC link voltage. Two control methods based on the fuzzy approach are applied and compared. First, a direct Mamdani fuzzy logic controller is presented. Then, a T-S fuzzy controller is elaborated based on a T-S fuzzy model. The Lyapunov theorem and H-infinity performance are exploited for stability analysis. Besides, the feedback controller gains are determined using linear matrix inequality tools. Simulation results are derived in order to prove the robustness of the proposed control algorithms and to compare their performances.

Heart disease diagnosis based on mediative fuzzy logic.

Mediative fuzzy logic is an approach able to deal with inconsistent information providing a solution when contradiction exists. The aim of this paper is to design an expert system based on this type of fuzzy logic in order to diagnose a possible heart disease for a patient. Our proposed system is an extension of the standard Mamdani fuzzy logic controller and contains 44 rules of the type single input-single output. The system works with 11 variables as inputs and one variable as output.

Seeker optimization approach to dynamic PI based virtual impedance drooping for economic load sharing between PV and SOFC in an islanded microgrid

Nowadays Load Sharing has been an emerging trend, necessary for the integration of multiple distributed generators (DGs) into the microgrid system through sophisticated power electronics converters. Proper sharing of load ascertains the utilities with increased energy cost efficiency, reliability, stability and protects the system from being overloaded. This work puts forth a novel control technique for proportional load sharing among parallel VSCs connected to an islanded microgrid in a distributed generating system consisting of Photovoltaic (PV) and Solid Oxide Fuel Cell (SOFC) as two micro sources. For tracking maximum solar energy a Mamdani Fuzzy Logic Controller (MFLC) based self-tuned Dynamic PI Controller (DPIC) is implemented for the Modified Perturb and Observe MPPT method. Also for the optimum cost management of the microgrid system, DPIC based communication less Virtual Impedance Drooping (VID) technique is implemented for suitable load sharing between the two hybrid micro sources. In order to further enhance the performance of the DPIC, the Membership function parameters are optimized using a heuristic computational algorithm named Seeker Optimization Approach (SOA). SOA computes the optimum value of the Membership function parameters for the MFLC and thus avoids the use of constant input valued parameters. The dynamic response and stability of the system with proposed method is compared and contrasted with DPIC during load sharing for ensuring robust control under conditions of linear and nonlinear loads. Validation and justification of the improvements achieved by the proposed controller are realized using MATLAB/Simulink environment.

Implementation of Mamdani Fuzzy Logic Control System on DC Motor Speed Controller

Abstract— Drive system is an important role important role in industrial processes, especially in electrical control. Maintaining a DC motor speed is a control system task that requires several method. Generally, set-point is defined as point of demand, and its comparation against process value (current speed) resulted in error. Both error and delta-error are two parameters required to the control system to determine system behavior in correction action. Such system of controller is a useful component to suppress the error signal so that the desired performance can be obtained. This research designs system of DC motor rotation speed control using Arduino Uno microcontroller to meet control specification on laboratory scale, implementing control application and Fuzzy Control System as control system algorithm.Because of its ability to be easily modeled using human intuitive, adaptive, does not require complex mathematical equations, not limited to linear or constant systems, and easily adapted to human input, Mamdani Fuzzy Logic Control System is used.