Fuzzy Logic Design Research Articles

Evaluation of physical risk factors by fuzzy failure mode and effects analysis: an apparel mill example

This study investigates the evaluation of risks faced by employees in a selected large-scale apparel mill using a risk assessment method with a fuzzy logic approach. The study found that risk assessment in the apparel industry is more accurate and reliable when using a fuzzy logic approach. Measurements were taken in three different time periods for noise, vibration, thermal comfort and lighting, which are physical risk factors. To evaluate the identified failure modes, the Failure Mode and Effects Analysis (FMEA) method was first applied. Then, for the same failure modes, the fuzzy FMEA method was applied using Fuzzy Logic Designer, an add-on to MATLAB. The study concludes that the fuzzy FMEA method can be a more useful method for risk assessment in the apparel industry.

Testing and analysis of a D-STATCOM prototype connected to the electrical network

In this article Flexible AC Transmission Systems (FACTS) devices are addressed, especially the design and implementation of a D-STATCOM prototype, making a comparison between the theoretical behavior and the aforementioned prototype. A fuzzy logic-based strategy was used for voltage control, employing the Matlab Fuzzy Logic Designer tool. The neural network was trainned with the network voltage, the voltage of the inverter stage, the current, the active and reactive power, and the power factor as inputs. Initially, the load power factor was 0.62 and the prototype operation allowed the voltage to be increased above the grid voltage, which is reflected in the forward current, modifying the power factor to a value of 0.91 while maintaining the same voltage. Finally, the results demonstrate the relationship between the increase of the inverter stage of the prototype above the electrical power line, obtaining a variable capacitor thus adjusting the power factor.

Synergistic prediction of penetration rate in Boukhadhra mining using regression, design of experiments, fuzzy logic, and artificial neural networks

Synergistic prediction of penetration rate in Boukhadhra mining using regression, design of experiments, fuzzy logic, and artificial neural networks

Fuzzy Classification of the Maturity of the Orange (Citrus × sinensis) Using the Citrus Color Index (CCI)

The orange (Citrus sinensis) is a fruit of the Citrus genus, which is part of the Rutaceae family. The orange has gained considerable importance due to its extensive range of applications, including the production of juices, jams, sweets, and extracts. The consumption of oranges confers several nutritional benefits, including flavonoids, vitamin C, potassium, beta-carotene, and dietary fiber. It is crucial to acknowledge that the primary quality criterion employed by consumers and producers is maturity, which is correlated with the visual quality associated with the color of the epicarp. This study proposes the implementation of a computer vision system that estimates the degree of ripeness of oranges Valencia using fuzzy logic (FL); the soluble solids content was determined by refractometry, while the firmness of the fruit was evaluated through the fruit firmness test. The proposed method was divided into five distinct steps. The initial stage involved the acquisition of RGB images. The second stage presents the segmentation of the fruit, which entails the removal of extraneous noise and backgrounds. The third and fourth steps involve determining the centroid of the fruit, and five regions of interest were obtained in the centroid of the fruit of the Citrus Color Index (CII), ranging from 3 × 3 to 11 × 11 pixels. Finally, in the fifth step, a model was created to estimate maturity, °Brix, and firmness using Matlab 2024 and the Fuzzy Logic Designer and Neuro-Fuzzy Designer applications. Consequently, a statistically significant correlation was established between maturity, degree Brix, and firmness, with a value greater than 0.9, using the Citrus Color Index (CII), which reflects the physical–chemical changes that occur in the orange.

Design of fuzzy logic controlled zero voltage switching based interleaved high gain converter

The conversion of low level DC voltage to high level DC voltage at lower duty ratios with less ripple current at source side is a challenging task in the distributed generation systems. Active switched inductor (ASL) converter is a high gain converter and has a large amount of input ripple current due to the parallel charging of the inductors during on time of the switches and series discharge of the inductors during off time of the switches. This large input ripple current in the converter when used in distributed generation (DG) affects the life time of low voltage batteries and fuel cells, the efficiency of photovoltaic source, increases the size and losses in the filtering stages and decreases the power density of the converters. In this paper, soft switching of interleaved active switched inductor (IASL) through Zero Voltage Switching (ZVS) is designed to overcome the limitations of ASL. The performance is analyzed in terms of its gain, input current ripple, switching loss and efficiency. An M-type resonant switch is used to achieve soft switching through ZVS technique.A proper value of resonant elements (inductor and Capacitor) is designed to achieve ZVS to improve the efficiency. Regulated voltage of proposed converter is required in DG systems and is possible with closed loop control. Fuzzy Logic controller (FLC) is designed to regulate the voltage and its dynamic performance is analyzed for different load values and also for sudden changes in load. Hardware model of the proposed ZVS based IASL converter with FLC is implemented and the results are verified with simulation results obtained from MATLAB Simulink.

Fuzzy Logic Method for Measuring Sustainable Decent Work Levels as a Corporate Social Responsibility Approach

The purpose of this study was to propose an interactive computer system that utilises the MATLAB Fuzzy Logic Designer to measure the level of implementation of SDG 8, which focuses on sustainable decent work (SDW) and economic growth. This study used policies and laws as parameters to determine the presence or absence of SDW. The fuzzy method was implemented in car windshield manufacturing in the auto parts industry as a case study to define and quantify work conditions and to determine the level of sustainable decent work (SDWL). The study described environmental conditions, such as noise, lighting, and heat stress; ergonomic factors, such as exposure time, the mass of the object manipulated, and lifting frequency; and organisation at work, such as workplace violence, salary, and workday, as linguistic variables. The level of the presence or absence of SDW was defined as their membership functions. The resulting vectors determined the absence of SDW with a score of 1.5 in two linguistic variables: environmental conditions and ergonomic factors. Some features of SDW in the linguistic variable organisation at work had an SDW score of 5. The SDWL vector determined a final score of 1.24, indicating the absence of decent work in production areas. This study found that the workers suffer a lack of long and healthy lives and a bad standard of living without economic growth due to work-related musculoskeletal disorders and work illnesses, increasing their out-of-pocket spending and catastrophic health expenses. As a CSR approach, assessing SDWLs helped managers improve policies and work conditions.

Design of Fuzzy Logic based Adaptive Active Power Controller to Enhance Power sharing among DGs in an Autonomous Microgrid

This paper presents the design of an adaptive active power controller to enhance the power-sharing capabilities of distributed generators (DGs) in an autonomous microgrid. Each DG in an autonomous microgrid consists of a droop-controlled inverter to control active and reactive power by regulating frequency and voltage correspondingly. The high droop gain can be used in the power controller to encourage faster power sharing among the DGs. However, high droop gain can cause undamped growing oscillations during load fluctuations or generation losses. During such events, attaining faster power-sharing between DGs using high droop gains is difficult. So, the problem with an autonomous microgrid is the conflict between faster power sharing and stability. Stability needs to be compromised to attain quicker power sharing and vice versa. Hence, to achieve power-sharing swiftly with high droop gain and to diminish the growing oscillations caused, this paper proposes a fuzzy logic-based adaptive active power controller (FLAPC). The proposed FLAPC is adaptive and easy to implement. It offers faster power sharing for different values of droop gains and step change in load. The FLAPC is developed in MATLAB 2018a/Simulink environment, and time domain simulations are performed to see the efficacy of the proposed controller. The results of time domain simulations are compared with a droop controller without any additional controller, conventional lead-lag power system stabilizer, and proposed controller for step change in load at different droop gains. The results show that the proposed controller enhances the power-sharing performance and also ameliorates the system’s stability by reducing the settling time and overshoot in active power responses of DGs.

Design and Implementation of Fuzzy Logic for Obstacle Avoidance in Differential Drive Mobile Robot

Autonomous mobile robots based on wheel drive are widely used in various applications. The differential drive mobile robot (DDMR) is one type with wheel drive. DDMR uses one actuator to move each wheel on the mobile robot. Autonomous capabilities are needed to avoid obstacles around the DDMR. This paper presents implementing a fuzzy logic algorithm for obstacle avoidance at a low cost (DDMR). The fuzzy logic algorithm input is obtained from three ultrasonic sensors installed in front of the DDMR with an angle difference between the sensors of 45$^0$. Distance information from the ultrasonic sensors is used to regulate the speed of the right and left motors of the DDMR. Based on the test results, the Mamdani inference system using the fuzzy logic algorithm was successfully implemented as an obstacle avoidance algorithm. The speed values of the right and left DDMR wheels produce values according to the rules created in the Mamdani inference system. DDMR managed to pass through a tunnel-shaped environment and reach its goal without hitting any obstacles around it. The average speed produced by DDMR in reaching the goal is 4.91 cm/s.

Comparison of Fuzzy Logic Control and Model Predictive Control for a Smart Adaptive Cruise Control Vehicle System

Adaptive Cruise Control (ACC), Cruise Control (CC), and Automatic Emergency Braking (AEB) serve as the basis of longitudinal automated driving, and as Adaptive cruise control (ACC), cruise control (CC), and automatic emergency braking (AEB) serve as the basis of longitudinal automated driving, and as such have been the subject of much research. Model predictive control (MPC) and fuzzy logic are often considered to be the next steps in improving the capability of these systems, but the two control strategies have not been compared to each other in the ACC, CC and AEB applications. Also, the three features (ACC, CC and AEB) have never been compiled into a single fuzzy logic controller. The purpose of this paper is to design a fuzzy logic-based ACC, CC, and AEB controller and compare it to an equivalent MPC controller. All three controllers control the desired longitudinal acceleration, and their functionality is tested using Matlab’s Fuzzy Logic Designer and other Simulink toolboxes. Ultimately, the results of the analysis demonstrate that the proposed fuzzy controller operates just as well if not better than the MPC controller and that the fuzzy controller is able to operate well in all tested scenarios.

Design of Fast Intelligence Hybrid Fuzzy Logic and Improved Incremental Conductance Based MPPT Technique

Presently, there has been a significant growth in the solar PV technology due to various factors such as clean technology, zero carbon emission and low maintenance. However, it becomes significant to extract the power in abundance from solar PV system due to its intermittent nature. To extract the significant power, a suitable maximum power point tracking (MPPT) technique has been presented. The MPPT technique is developed based on the hybrid fuzzy logic (FL) and improved incremental and conductance (IInC) method. The aim of developing hybrid FL-IInC technique for MPPT is to regulate duty cycle so that maximum power point (MPP) is achieved with stable and accurate outcomes during dynamic and steady state conditions. The IInC method was developed by regulating the duty cycle and step-size. of It was observed that proposed hybrid FL-IInC delivers accurate and stable results as compared with IInC method. During the model testing at the irradiance with 1000w/m2, the achieved MPPT efficiency was 99.96% as compared with the IInC with an outcome of 98.47% for the improved technique. Additionally, the settling time for the proposed hybrid FL-IInC was less compared with the IInC method. All the simulations were conducted with the MATLAB Simulink Platform. In future, the hybrid FL-IInC method can be employed with other conditions such as partial shading for better applicability

Prediction and optimization of process parameters using design of experiments and fuzzy logic

Prediction and optimization of process parameters using design of experiments and fuzzy logic

Development of a supply chain model for the production of biodiesel from waste cooking oil for sustainable development

The increasing demand for energy and the severe environmental and economic repercussions have contributed to the development of renewables options. The scarcity of fossil fuels and their negative effect on the environment have sparked an alarming situation for alternative energy sources that are cleaner and more sustainable. Waste cooking oil is a valuable feedstock for biodiesel production, but it is often disposed of improperly, causing environmental pollution and health hazards. The current waste cooking oil supply chain in Pakistan and other countries is fragmented, inefficient, and often unregulated, leading to a lack of standardization and quality control. The study aims to develop a comprehensive supply chain model that integrates waste cooking oil collection, transportation, processing, and biodiesel production to create a sustainable value chain that benefits the environment, the economy, and society as a whole. The proposed optimization approach reduces the total expenses associated with the activities of the biodiesel supply chain. Modified possibilistic chance constrained programming (MPCCP) is used as a solution technique to represent this uncertainty. The MPCCP model is solved with the assistance of Lingo 18.0, while fuzzy logic demand forecasting was done using MATLAB. Accordingly, the fuzzy logic designer (FLD) simulation was conducted to demonstrate the applicability and effectiveness of FLD simulation for the particular kind of issue being considered. The research, not only focuses on mitigating environmental and health risks associated with improper waste cooking oil disposal, resulting in reduced pollution and a cleaner environment but it also advocates for the efficient utilization of waste cooking oil as a valuable feedstock for biodiesel production, thereby promoting a more sustainable and renewable energy source. By optimizing supply chain activities and minimizing costs, the research contributes to enhancing economic growth and efficiency within the biodiesel industry. This research encourages further exploration and collaboration among researchers and stakeholders to expand the applications of the proposed model in waste management, renewable energy, and supply chain optimization.

Bespoke Fuzzy Logic Design to Automate a Better Understanding of Running Gait Analysis.

Running gait assessment and running shoe recommendation is important for the injury prevention of runners who exhibit different skill-levels and running styles. Traditionally, running gait assessment for shoe recommendation relies upon a combination of trained professionals (e.g., sports-therapists, physiotherapists) and complex equipment such as motion or pressure sensors, often incurring a high-cost to the consumer. Despite this, assessments are still prone to subjectivity, and may differ between assessors. Alternatively, methods to provide low-cost, reproduceable gait assessment has become a necessity, especially within a habitual (low-resource) context, with many traditional methods generally unavailable due to the need of professional assistance and more recently the COVID-19 pandemic. Fuzzy logic has shown to be an effective tool in the assessment and identification of gait by providing the potential for a high-accuracy methodology, while retaining a low computational cost; ideal for applications within embedded systems. Here, we present a novel shoe recommendation fuzzy inference system from the classification of two key running gait parameters, foot strike and pronation from a single foot mounted internet of thing (IoT) enabled wearable inertial measurement unit. The fuzzy approach provides excellent (ICC > 0.9) accuracy, while significantly increasing the resolution of the gait assessment technique, providing a more detailed running gait analysis.

ІНФОРМАЦІЙНО-АНАЛІТИЧНА СИСТЕМИ ПРОГНОЗУВАННЯ ВРАЗЛИВОСТЕЙ ТА ЗАГРОЗ ІНФОРМАЦІЙНОЇ БЕЗПЕКИ

The paper proposes a block diagram of an information-analytical system for predicting vulnerabilities and threats to information security. The analysis of the conducted research allows us to conclude that in order to solve the problem of research and development of an information-analytical fuzzy system for a logical fuzzy conclusion about the emergence of vulnerabilities and threats to information security, automating the analysis of the message flow of thematic Internet resources, it is advisable to use expert forecasting systems. To solve the problems of predicting vulnerabilities and threats to information security of confidential data based on the flow of thematic messages of Internet resources using the proposed algorithms and method, hybrid-type expert forecasting systems designed for use on general-purpose information and computer technology can be used. The algorithm for predicting vulnerabilities and threats to information security implemented in the information and analytical system based on the analysis of the data flow of thematic Internet resources allows automating the information process of detecting new vulnerabilities and threats, provides information security specialists with the opportunity to assess the degree of security of resources in a timely manner and, if necessary, take appropriate measures for neutralization possible threats and vulnerabilities, thereby increasing the information security of computing computer systems against the implementation of new network computer attacks. An analysis of fuzzy logical inference systems, modern tools for processing large volumes of data, tools for morphological text analysis, and ontology editors was conducted. UML diagrams of activities, sequences of actions, and classes were built to carry out logical modeling of the information system for forecasting vulnerabilities and threats to information security. For the physical modeling of the system, UML-diagrams of deployment and components have been developed. The possibility of implementing an information-analytical system for predicting vulnerabilities and threats to information security based on the analysis of text messages of thematic Internet resources using the following software products is substantiated: DBMS MySQL, ontology editor - Protégé, fuzzy logic inference system - Fuzzy Logic Designer, morphological data analysis tools - Mystem. To evaluate the obtained results, the indicators MAPE, MAE, RMSE for the values of forecasting the occurrence of vulnerabilities and information security threats, as well as smoothed time series calculated on their basis with a period of three and five.

Fuzzy Logic Controller for Temperature Control

This paper describes the use of MATLAB in the design and implementation of a temperature control system for agricultural applications based on fuzzy logic. The system uses two inputs—the user-specified target temperature and the temperature sensed from the agricultural field—to produce two outputs for positive and negative pulse width modulation (P-PWM-C and N-PWM-C). With membership functions defined for the input and output ranges, the fuzzy logic controller is designed using the MATLAB Fuzzy Logic Designer. Ten guidelines are created to regulate the temperature control procedure. The relationship between the inputs and outputs is shown in the 3D surface viewer, which sheds light on the fuzzy logic control approach.

Genetically Optimized Pitch Angle Controller of a Wind Turbine with Fuzzy Logic Design Approach

An important engineering challenge is the design of a wind turbine’s pitch angle controller. The dependability, safety, and power output maximization of a wind turbine are all impacted by this controller. In this study, a 2 MW doubly fed induction generator wind turbine’s blade angle controller design with a novel fuzzy logic controller is tested in a simulated environment. The evolutionary algorithm technique is used to optimize the fuzzy logic controller with three inputs. A genetic algorithm is used to optimize the specified pitch angle controller for a number of coefficients. After the optimization process, the controller’s performance is assessed in terms of power output, overshoot, and steady-state error characteristics.

Design and real-time validation of PI and Fuzzy Logic tuned photovoltaic integrated DSTATCOM to improve power quality.

Power loss and malfunctioning of equipment due to current and voltage harmonics produced by nonlinear loads are major concerns for both power suppliers and consumers. This paper employs DSTATCOM to mitigate these harmonic distortions in distribution systems. DSTATCOM is a current controlled voltage source PWM converter with passive components. It supplies compensating current that eliminates current harmonics and provides reactive power compensation thereby providing a smooth source current. The performance of DSTATCOM is controlled using proportional integral and fuzzy logic-based control (FLC) schemes to improve power quality at dynamic loading. As the penetration of photovoltaic (PV) systems in energy production is significant, the power quality issues for PV-integrated systems have also been investigated. The maximum power point for a PV array is tracked using perturb and observe and FLC methods. The complete system is simulated in MATLAB and extensively investigated for a large range of balanced and unbalanced nonlinear load conditions. The simulation results are validated in real time through the hardware in loop (HIL) test bench using Opal-RT controller. The proposed PV-integrated DSTATCOM with FLC yields better performance in terms of DC link voltage settling time, total harmonics distortion, and smooth source current as compared to other schemes.

A KNOWLEDGE-BASED DECISION SUPPORT SYSTEM FOR SELECTION OF SUSTAINABLE STRUCTURALMATERIALS: USINGFUZZY EXPERT SYSTEM

One of the most critical elements in the construction industry is the materials used in the structure. The selection of appropriate materials can significantly impact cost, durability, beauty of the building, and many other aspects. Given the importance of sustainable development, it is necessary to carefully examine all the sustainability dimensions, including economic, social, environmental, and technical, which impact both the construction and operation phases of a building lifecycle. This study develops a Fuzzy Expert System (FES) as a knowledge-based decision support system (KBDSS) to rank structural materials according to sustainability criteria. A total of twelve sub-criteria are considered, three under each sustainability criterion. The FES is divided into five sub-systems to reduce the number of rules. A total of 243 rules are implemented in the model using MATLAB Fuzzy Logic Designer Toolbox. A case study was performed to evaluate four structural materials for sustainability criteria. These materials are: timber, concrete, steel, and blended cement. The results of the case study show that timber is the most sustainable material according to user input. However, the developed model can be easily calibrated to reflect changes in experts’ opinions, leading to a different ranking of structural materials.

FUZZY REGULATOR OF THE ROD SUPPLY SYSTEM OF ARDUINO-BASED CROSS-WEDGE ROLLING MILL IN LABVIEW ENVIRONMENT

The main purpose of scientific work is to develop a regulator based on fuzzy logic for an electronic control system for the supply of bar billets in automated cross-wedge rolling complexes.
 Cross-wedge rolling belongs to the advanced energy-saving technologies where technological processes based on metal cutting are replaced by economic processes of plastic forming of details. The analysis showed that coefficient increase of metal utilization requires loss decrease arising from the supply inaccuracy of the bar workpiece to the length of the part. The task is to reproduce the given law of motion of the actuator, i.e. to ensure maximum speed, smooth acceleration and braking with the possibility of a shockless stop at the end of the stroke. The solution of this problem is proposed to be carried out by entering the fuzzy control unit into the structure of the system for controlling the supply..
 The hardware basis of the control system in this work is the Arduino hardware and software complex based on the Atmel AVR microcontroller. The software model of the control system is developed in the graphical programming environment LabVIEW, which is widely used as a standard tool for data collection and control of devices in industry, education and research laboratories. The construction of a fuzzy regulator and its implementation are considered using Fuzzy Logic Designer.
 On the basis of systematized knowledge about the object of research, its characteristics and features, linguistic input and output variables were selected, as well as membership functions and a fuzzy production model of knowledge was formulated. The configuration of the control system depends on the number of controlled outputs and controlled input data and in this case the MISO information structure is applied. The paper presents the front panel of the virtual device developed by LabVIEW, which is designed to display information about the current state of the system. The purpose of the developed fuzzy regulator model is to output the value of the initial air pressure in the cavity of the actuating pneumatic cylinder, which will create an air cushion in the process of advancing the piston, which has properties similar to those of a normal spring. Smooth stopping of the piston is provided by the coordinated choice of rigidity of a pneumatic spring taking into account length of a detail and weight of a rod and allows us to reduce kinetic energy in an end point of positioning that increases accuracy of supply.
 The analysis of research results confirmed the efficiency and adequacy of the model used and the developed set of operation rules of the fuzzy regulator. Testing of the control system on parts of different lengths confirmed a reduction in total losses and an increase in the utilization of the metal.

Design of fuzzy logic based energy management and traffic predictive model for cyber physical systems

Cyber-Physical Systems (CPS) is a multidisciplinary effort that links cyber and physical vectors. Cyber systems may be incorporated into any physical system, such as intelligent transportation systems (ITS), industries, and cities, to improve its intelligence, energy efficiency, and comfort. The design of self-driving cars has a large influence on vehicle travel demand and energy consumption. ITS also heavily relies on the Traffic Flow Prediction (TFP) system. With this rationale, this work focuses on the development of a Fuzzy Logic-based Energy Management and TFP model (FLEM-TFP) for CPS in ITS. The FLEM-TFP approach proposed here involves two primary processes: energy management and TFP. An adaptive neuro-fuzzy inference system (ANFIS) model is also utilized to compute the engine torque required based on various readings. In addition, a sailfish optimizer (SFO)-based fuzzy wavelet neural network (FWNN) approach is used in the ITS to estimate traffic flow. According to the findings of the experiments, the FLEM-TFP approach outperformed previous state-of-the-art procedures.