Fuzzy Logic Control Model Research Articles

Effectiveness of the Fuzzy Logic Control to Manage the Microclimate Inside a Smart Insulated Greenhouse

Agricultural greenhouses incorporate intricate systems to regulate the internal climate. Among the crucial climatic variables, indoor temperature and humidity take precedence in establishing an optimal environment for plant production and growth. The present research emphasizes the efficacy of employing intelligent control systems in the automation of the indoor climate for smart insulated greenhouses (SIGs), utilizing a fuzzy logic controller (FLC). This paper proposes the use of an FLC to reduce the energy consumption of a greenhouse. In the first step, a thermodynamic model is presented and experimentally validated based on thermal heat exchanges between the indoor and outdoor climatic variables. The outcomes show the effectiveness of the proposed model in controlling indoor air temperature and relative humidity with a low error percentage. Secondly, several fuzzy logic control models have been developed to regulate the indoor temperature and humidity for cold and hot periods. The results show the good performance of the proposed FLC model as highlighted by the statistical analysis. In fact, the root mean squared error (RMSE) is very small and equal to 0.69% for temperature and 0.23% for humidity, whereas the efficiency factor (EF) of the fuzzy logic control is equal to 99.35% for temperature control and 99.86% for humidity control.

Simulation of A Fuzzy Controller for Controlling A Two-Mass Electromechanical System

This study presents a model with a fuzzy logic controller (FLC) for controlling a two-mass electric drive operating with a dynamic load. In the Simulink environment of the MatLab software package, a model of the mechanical part of the electric drive system with a proportional integral differential (PID) controller was built, which was tested for two different characteristics created by the technological mechanism. The low performance of a system with a PID regulator has been proven, especially in electric drive systems operating with a shock load. The expediency of using Fuzzy logic regulators for electric drive systems operating under uncertain conditions is substantiated. To ensure the required efficiency of the electric drive system, a FLC model has been developed in the Fuzzy Logic Toolbox environment of the MatLab software package. To control the mechanical part of the electric drive system 16 laws have been formulated. Tests of the electric drive control system model with a FLC confirm its high efficiency when changing the load torque according to various laws. A hybrid model was synthesized with the help of the ANFIS editor. It has been confirmed that the hybrid network provides satisfactory identification quality.

Application of the Fuzzy Mamdani Method for Predicting Rainfall in the City of Yogyakarta

Weather and climate play an important role in everyday life, influencing various aspects of society and the environment. The city of Yogyakarta as a cultural center cannot be separated from the impacts of climate change, including rainfall patterns which can have significant effectiveness on daily activities, agriculture and infrastructure. This research uses a fuzzy logic methodology approach with the Mamdani method to predict rainfall. Fuzzy logic has proven to be effective in cold situations and is able to overcome the challenges of missing or incomplete data. The data used in this research was obtained from Bappeda DIY. The implementation for predicting rainfall levels uses Matlab by utilizing the provided Fuzzy Logic Matlab Toolbox. Based on research conducted using a fuzzy logic test to calculate all research accuracy data, an average error value in RMSE was obtained of 39.88%. These results indicate that there is significant error in these predictions, indicating that the fuzzy logic control model is not completely accurate. Therefore, it is necessary to review the data used in this prediction to ensure its accuracy.

Enhanced Fuzzy Logic Control Model and Sliding Mode Based on Field Oriented Control of Induction Motor

Enhanced Fuzzy Logic Control Model and Sliding Mode Based on Field Oriented Control of Induction Motor

Fuzzy logic control for watering system

A two-dimensional finite element (FEM) model was developed to simulate water propagation in soil during irrigation. The first dimension was water distribution depth in soil, and the second dimension was time. The developed model was tested by analyzing water distribution in a conventional (clock-controlled) irrigation model. The values in the conventional model were calculated based on the literature. The results were consistent with the results obtained from the model. In the next step, a fuzzy logic model for irrigation control was developed. The input variables were ambient temperature, soil moisture content and time of day (which is related to solar radiation and evapotranspiration), and the output variable was irrigation intensity. The fuzzy logic control (FLC) model was tested by simulating water distribution in soil and comparing water consumption in both models. The study demonstrated that the depth of the soil moisture sensor affected water use in the fuzzy logic-controlled irrigation system relative to the conventional model. Water consumption was reduced by around 12% when the soil moisture sensor was positioned at an optimal depth, but it increased by around 20% when sensor depth was not optimal. The extent to which the distribution of fuzzy variables affects irrigation performance was examined, and the analysis revealed that inadequate distribution of fuzzy variables in the irrigation control system can increase total water consumption by up to 38% relative to the conventional model. It can be concluded that a fuzzy logic-controlled irrigation system can reduce water consumption, but the system’s operating parameters should be always selected based on an analysis of local conditions to avoid an unintended increase in water use. A well-designed FLC can decrease water use in agriculture (thus contributing to rational management of scarce water resources), decrease energy consumption, and reduce the risk of crop pollution with contaminated groundwater.

Designing a Ship Autopilot System for Operation in a Disturbed Environment Using the Adaptive Neural Fuzzy Inference System

Efficient ship guidance, fuel savings, and reduced human control have long been a key focus in developing intelligent controllers. The integration of neural networks and fuzzy logic control offers numerous advantages, creating a robust and adaptive system capable of handling complex dynamics and uncertainties. This intelligent control system learns from its environment and adjusts behavior, making it effective in challenging situations. Additionally, it improves system efficiency, reduces energy consumption, and minimizes human intervention, enhancing safety and reducing errors. This study presents an intelligent control approach, titled “Designing a Ship Autopilot System for Operation in a Disturbed Environment using the Adaptive Neural Fuzzy Inference System”, combining a neural network and fuzzy logic control to steer ships. A 6DOF dynamic model is constructed, simulating ship operations with noise signals. The ANFIS controller comprises six layers, with a distinct composition rule expressing conclusions as linear equations of input variables. Layer 1 has two input signals, layer 2 represents fuzzy rules with six nodes, and layers 3, 4, and 5 contain nine nodes each. Layer 6 combines output signals from layer 5, following the first-order Takagi–Sugeno fuzzy logic control model. Simulation results using MATLAB/Simulink demonstrate the superiority of the ANFIS controller over the PID controller, significantly improving stability and trajectory accuracy.

Electric Bike Range Estimation using Fuzzy Logic Controller

Electric Vehicles (EV) are the prompt solution to significantly lowering the use of fossil fuels and CO2 emissions from the transport industry. There is a continuing growth in the number of EVs in use, but their huge acceptance by customers is associated to the quality they can provide. Nowadays, different types of electric vehicles are moving toward green awareness, and one among them is the electric motorcycle which is a considerable vehicle in India. Though there are many benefits of driving electric motorcycles, because of the limited driving range and inadequate charging stations, it is still not generally accepted in the industry. Range anxiety is the major market concern that is solved by the implementation of an additional range estimating technique that can ease the "range anxiety" caused by the restricted range of EVs. Therefore, this paper proposes a fuzzy logic controller model for the estimation of the EV range based on the battery's state of charge and the load's power usage. In this work, the load power consumption of the vehicle and the status of the battery charge are selected as inputs and the EV range is selected as a Fuzzy Logic Controller output. This model is implemented in the Matlab/Simulink environment.

Investigation of type 1 and type 2 fuzzy logic controllers performance: application of speed control of BLDC motor

In this paper, a simulation study enhanced to model that the speed control of brushless direct current (BLDC) motors used in electric vehicles with intelligent control methods. The simulation study was prepared in Matlab/Simulink environment. The first control method is Type-1 fuzzy logic control (T1FLC), and the second control method is the Intermittent Type-2 fuzzy logic control (IT2FLC) model. Membership functions for different membership numbers have been created for both types of FLC models. These are 3×3, 5×5, 7×7. Control methods are prepared in Matlab/M-file environment. The model is defined as the input variable of the error, which is the difference between the reference speed and the motor speed, and the output variable of the Pulse Width Modulation (PWM) signal applied to the motor. The simulation study maintains the speed of the BLDC motor up to the reference speed with T1FLC and IT2FLC controllers, depending on the reference speed and applied load values. Depending on the number of different memberships, the effects of controller performances on the control of motor speed have been observed. The graphs and findings of the experiment are shown in the results and discussion section.

Driving support by type-2 fuzzy logic control model

Advanced models of Artificial Intelligence enable systems of IoT to work with great flexibility to the needs of users. In this article we present our developed IoT system for driving support by the use of type-2 fuzzy logic control module. We have developed the IoT system to collect the data about driving conditions and evaluate them adjusting to the needs of user. Applied module of fuzzy logic of the second type was used in analysis of accelerometers signals to flexibly adjust to uncertainty of evaluation of driving expectations of each driver. Our developed system was tested in different cars by driving on various roads and results show excellent efficiency.

An optimal washout filter for motion platform using neural network and fuzzy logic

To experience the motion sensation of a real vehicle through a motion simulator, a motion cueing algorithm (MCA) is required to transform the vehicle motions to the driving motion platform (DMP) while respecting the physical limitations of DMP. In this aspect, the optimal washout filter (WF) extracts the optimal motion signals including linear accelerations and angular velocities for the DMP with consideration of the human vestibular model and DMP motion states using the linear quadratic regulator (LQR) technique. The LQR technique is employed to obtain the optimal and pre-defined higher order transfer functions by solving the Riccati equation. However, the Riccati equation is solved using fixed weights, leading to an inconvenient usage of the DMP workspace. In this research, a new optimal WF model is designed and developed using a neural network (NN) and a fuzzy logic controller (FLC). The NN is introduced to solve the Riccati equation online while the FLC model is designed to extract the weighting matrices of the LQR technique. The proposed technique considers the physical DMP limitations online and reproduces accurate motion signals with a high degree of fidelity. The results demonstrate the efficiency of the developed optimal WF model as compared with those of existing optimal WF models.

Applying fuzzy logic control to analyze real-time control for charging and discharging power of electric vehicles

The disorderly charging of large-scale electric vehicles will aggravate the peak-valley difference of the power grid, and affect the power quality and life of the transformer. The fuzzy logic control strategy for charging and discharging optimization of charging vehicles under the framework of fuzzy logic control from the perspective of the group is considered in this article. A real-time control method based on the clustering characteristics of the charging end time is proposed according to the different charging requirements of the connected electric vehicles and fuzzy logic control is adopted to solve the problem of optimal charging and discharging power of the entire cluster and a single electric vehicle. A fuzzy logic control model considering the charging and discharging of electric vehicles is established orienting at minimize daily load fluctuations and control penalties in the upper layer. The charging and discharging cost of electric vehicle owners is considered to solve the optimal control problem of the charging and discharging power of a single electric vehicle. Taking the data of the typical regional distribution network load as an example, it is verified that the real-time charging optimization strategy under fuzzy logic control through simulation can ensure the reliable operation of the power grid while considering the interests of all parties.

ATP-Based Fuzzy Logic Controller Model for Electromagnetic Transients Studies

Alternative Transient Program (ATP) has been steadily developed and improved in the last decades. Today, ATP Draw, a graphical framework, is available, which has improved the use of the program significantly. However, fuzzy controller is one of complex models for which a simplified model can be a very useful tool. This paper introduces a model for fuzzy logic controller in ATP for electromagnetic transients' studies of power system. The proposed model introduces a first model of fuzzy logic in ATP. This facilitates power system and fuzzy logic controller simulation in ATP without needing any other programs. The proposed model is implemented in ATPDraw environment. The proposed fuzzy logic controller model consists of three parts; Membership functions (triangular and trapezoidal shapes), Rules and Defuzzification. The proposed model is verified using numerical examples. The membership degree, the rules, and the defuzzification are calculated using both numerical calculation and the proposed model. The proposed model verification shows acceptable results.

Track the maximum power of a photovoltaic to control a cascade five-level inverter a single-phase grid-connected with a fuzzy logic control

<p>This research presents tracking the maximum power of a<em> </em>photovoltaic to control a five-level inverter, a cascade type connecting a single-phase grid-connected system with a fuzzy logic control model. Maximum power tracking control In this research, the principle of controlling the maximum current amplitude of the photovoltaic multiplied by the sine signal per unit that used as a reference current compared to the grid current. Signal comparison with the PID controller allows the creation of five levels of PWM of cascade control of five-level inverter connects single-phase grids. The results of the simulation test using the program MATLAB/Simulink to compare with the generated prototype found that the fuzzy logic principle was used to control the maximum power tracking conditions of the P&O method, when the amount of radiation light intensity decreases suddenly, making it possible to track the maximum power of the photovoltaic. Also, when<em> </em>the inverter connected to the grid by controlling the power<em> </em>angle to compare results between the simulation and the prototype — found that the current flowing into the grid increases according to the power angle control. Resulting in a nearby waveform, sine wave and an out of phase angle to the grid voltage because the system is in the inverter mode, and the harmonic spectrum of the grid currently has total harmonic distortion (THD) is reduced as an indication of the proposed system can be developed and applications.</p>

Modelling, analysis and control of an eddy current braking system using intelligent controllers

Combination of conventional brakes with Eddy Current Brake is the current trend in many applications where superior braking performance at high speed is desired. Eddy Current Brakes being frictionless and contactless offer numerous advantages over conventional brakes. This paper gives detailed insi ght into the hardware model development, analysis and control of a multi disc Eddy Current Braking System using different intelligent controllers. Firstly, Fuzzy Logic Controller has been developed which can give the feasible value of the electromagnet current required which leads to improved braking performance. Further, Artificial Neural Network Controller has been designed for existing hardware system which gives better, reliable, efficient results in comparison to the Fuzzy Logic Controller and hardware reference model for the sample period of time.

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.

A Fuzzy Logic Controller Approach for Controlling Heat Exchanger Temperature

This paper presents a fuzzy logic controller approach for controlling heat exchanger temperature. Fuzzy logic controller is an artificial intelligence-based controller. The fuzzy logic controller has been widely used for control applications in the industrial world. One of the tools used in the industrial world that requires accurate control is the heat exchanger. A heat exchanger is a device used to process the mixing of liquids that have different temperatures. In this case, temperature control becomes very important. Fuzzy logic control is applied to the heat exchanger so that the mixed fluid has a constant temperature. Fuzzy logic control models in this study are combined with neural network techniques. The fuzzy logic controller model is simulated in Matlab software. The results showed that the fuzzy logic controller was able to stabilize the temperature of the heat exchanger well.

An autonomic resource provisioning framework for efficient data collection in cloudlet-enabled wireless body area networks: a fuzzy-based proactive approach

Integrating wireless body area networks (WBANs) with cloudlet introduces an edge-of-things computing environment for pervasive applications. The variation in the number of active WBANs nodes and its data transmission rate requires optimal computing resources to avoid performance degradation and data loss. We argue the research gap in terms of optimal resource provisioning that predicts and automatically adjusts the computing resources on the basis of sensory data volume and application’s type. In this paper, we propose a hybrid autonomic resource provisioning framework, which is the combination of autonomic computing, fuzzy logic control and linear regression model. The proposed framework is built over CloudSim toolkit with autonomic resource provisioning framework inspired by the cloud layer model. The effectiveness of the proposed approach is evaluated under a real workload trace. The experimental results show that the proposed approach minimizes the cost by at least 27% and SLA violations by at least 78% as compared to other approaches.

Fuzzy Adaptive Control Applied to a Hybrid Electric-Power Generation System (HEPGS)

Objectives: Application of a fuzzy adaptive control to a commuted Hybrid Electric Power Generation System (HEPGS) for different case studies. Methods: The system is integrated by a 400 W wind turbine, a Polymer Electrolyte Membrane Fuel Cell (PEMFC), and a solar photovoltaic panel which represent the three sources of energy of the system, where the oxygen and hydrogen flow are controlled variables, and the wind power, solar power and electrical demand are uncontrolled variables considering two meteorological station, Puerto Bolivar and Ernesto Cortissoz. Findings: The transient responses of the system with respect to the variation of the solar radiation and wind velocity, regarding to the meteorological and geographical conditions of the zone, are presented as results of the manipulation of the reactant flow in the PEMFC. It was possible to propose an adaptive fuzzy PID which responses were compared to the standard responses of achieved by a classical PID controller, the responses of the fuzzy adaptive PID were 37,4 and 45,4% improved for the meteorological stations Puerto Bolivar and Ernesto Cortissoz respectively for an electrical demand of 200 MW. Finally, a fuzzy logic control model was proposed and tested, demonstrating an improved performance than a classical PID. Application: These systems are able to be applied in rural zones where the on grid energy systems are not supplied. In addition, a hybrid energy system may be improved through the implementation of novel control techniques, so that can be possible to increase the efficiency and improve the performance of the HEPGS. Keywords: Adaptive Fuzzy PID, Fuel Cells, Hybrid Energy System, Solar Energy, Wind Energy

Design and real time implementation of adaptive neural-fuzzy inference system controller based unity single phase power factor converter

This paper presents a design and a real time application of an efficient adaptive neural-fuzzy inference system based voltage controller for a single-phase boost unity active power factor correction in order to improve its performances. Basically, the adaptive neural-fuzzy inference system is a combination of fuzzy logic and artificial neural network techniques. The proposed control improves the DC bus voltage loop and presents a good capacity to track the voltage reference point under a fast variation of the load with less fluctuation in the steady state. The adaptive neural-fuzzy inference system training datasets are extracted from the fuzzy logic controller model developed in MATLAB Simulink and its robustness has been verified experimentally under different measurement noises and disturbances. This technique presents good performances comparing with others approaches in terms of total harmonic distortion, power factor, the response time and the accuracy in the steady state under different parameters variation, non-linearity and the load change effect.

Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller.

The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.