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Electric Arc Furnaces (EAFs) are widely used in the steel manufacturing industry to melt scrap steel by employing a large number of electric arcs. EAFs play an important role in ensuring the efficient production of steel. However, their nonlinear and variable load characteristics have a significant impact on power quality. Because the active power of an electric arc depends on its length, a system for controlling the electrode positions is necessary. This paper presents a control system based on a fuzzy logic controller for the active power control of an electric arc furnace. Individual simulation scenarios were chosen with both reference values and the process taken into consideration. The reference, constant value, step variation, and the sequence of step variation were investigated, as well as step disturbances and the sequence of step disturbances from the viewpoint of the process. Furthermore, the procedure of changing the tap on a transformer was investigated. The proposed solution minimizes the time required for charge elaboration, but the main benefit is that there are no additional costs in the implementation process because the installation remains identical, with the only changes being improvements to soft control management. [ABSTRACT FROM AUTHOR]

Transfer of laboratory-scale experiments to production-scale ethanol fermentation is time-consuming and involves expensive prototype systems from complex experimental designs that determine optimal operating conditions for minimal substrate and product inhibitions. The study developed and validated a Simulink-based model for optimal pH and temperature control using fuzzy logic and PID controllers respectively and taking advantage of 2D and 1D substrate and product inhibition models from which suitable ethanol fermentation reaction rates models were selected. Temperature and pH levels and substrate, product, and biomass concentrations were measured. Selected inhibition models were linear-product, linear substrate-sudden stop product, and linear substrate for cassava, maize, and sorghum, respectively. Fuzzy logic controller ascertained optimal flow rate of acid and base as 0.000196 ml/s and 0.000204 ml/s, respectively, and pH error and rate of pH error as 0.00334 and 0.00368, respectively. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1905; maize: F critical 0.9704, F calculated = 0.2149; sorghum: F critical = 0.9704, F calculated = 0.2488). PID logic controller showed model curves and experimental curves with good fit. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1288; maize: F critical = 0.9704, F calculated = 0.2083; sorghum: F critical = 0.9704, F calculated = 0.2016). The study provided an improved approach as solution for optimal pH and temperature conditions in order to mitigate substrate and product inhibitions during ethanol fermentation. It illustrated that the application of artificial intelligence-based controllers provides satisfactory outcomes that are desirable for implementation in the industrial space. Highlights: ✓ Inhibition models were selected for three feed stocks using the model fitness coefficient and applied to ethanol fermentation batch dynamic model. ✓ Selected inhibition models included the following: linear-product, linear substrate-sudden stop product, and linear substrate for cassava, maize, and sorghum, respectively. ✓ Fuzzy logic controller determined optimal pH profile, flow rates of acid and base, pH error, and rate of pH error. ✓ There was no significant difference between model and experimental profiles (cassava: F critical = 0.9704, F calculated = 0.1905; maize: F critical 0.9704, F calculated = 0.2149; sorghum: F critical = 0.9704, F calculated = 0.2488). ✓ PID logic controller determined optimal temperature profile. ✓ There was no significant difference between model and experimental profiles (cassava: F critical = 0.9704, F calculated = 0.1288; maize: F critical = 0.9704, F calculated = 0.2083; sorghum: F critical = 0.9704, F calculated = 0.2016). [ABSTRACT FROM AUTHOR]

This article proposes a fuzzy-based Unified Power Quality Conditioner (UPQC) with Wind Power Generation (WPG) and Battery Storage System (BSS). The UPQC contains a series and a shunt voltage source converters (VSC) coupled with a dc-bus. The hybrid controller adopts both the fuzzy and proportional integral (PI) controllers. The prime objectives of the present work are: (1) quick response and dc-link voltage levelling; (2) removal of supply voltage sag/swell; (3) reduction of harmonics and enhancement of power factor; (4) satisfactory performance for severe disruptions; and (5) adequate compensation for unbalanced systems. The performances of the suggested controller are examined for eight test scenarios involving various load configurations, and distorted grid voltages, and compared to those of the existing PI, sliding mode, and fuzzy logic controllers. It portrays that the proposed hybrid controller lowers the THD values to the best possible values and improves the power factor to almost unity for different loads with several power quality issues in addition to requiring very less time for reaching the steady dc-bus voltage. [ABSTRACT FROM AUTHOR]

Summary: Wireless body area network (WBAN) is a potential low‐cost technology for privacy‐sensitive telemedicine and e‐health monitoring and services. However, it faces limited protocol and physical resource support challenges, which can result in packet transfer difficulties. In particular, WBAN requires an emergency‐aware technology that ensures a promising quality of service (QoS). One significant issue affecting QoS and energy efficiency in WBAN is congestion. Effective congestion control techniques are essential for achieving proper load balancing. To address these challenges, we propose a congestion severity aware rate control (CSRR) algorithm that enhances packet transmission rate by reducing packet losses and retransmissions. The CSRR algorithm incorporates a fuzzy controller to predict congestion rates based on runtime metrics. To regulate congestion window growth in different algorithm phases, we introduce sequences such as the Fibonacci retracement sequence, knight's move sequence, and the binary logarithm of the Nth primorial sequence to regulate congestion window growth in the different phases of the proposed algorithm. We mathematically analyze the proposed CSRR algorithm using a Markov model. The simulation results demonstrate the superiority of our algorithm compared to existing approaches. Specifically, our algorithm achieves significant optimizations in terms of throughput (52.92%), packet loss (38.11%), delay (37.23%), and remaining energy (36.86%) when compared to existing algorithms. [ABSTRACT FROM AUTHOR]

This article describes a research study on an electromechanical system with saturation, where a fuzzy hybrid controller with integral action and anti-windup is applied. The study focuses on implementing this Integral Fuzzy Logic Controller (IFLC) on a Field-Programmable Gate Array (FPGA) board. The fuzzy controllers, known for their effectiveness in handling disturbances and saturations, are used in a parallel structure. To optimize the performance of the controller, the Particle Swarm Optimization (PSO) technique is employed to tune the membership functions and feedback loop gains. The complex algebraic concepts and Type 1 fuzzy logic algorithms are transformed into mathematical equations suitable for VHSIC Hardware Description Language (VHDL). The proposed controller is co-simulated using Vivado and Xilinx® System Generator (XSG) tools on both software and hardware platforms. The utilization of fixed-point data propagation in the controller's structure ensures optimized implementation methods. The performance index of our controller surpasses that of a conventional Proportional-Integral-Derivative (PID) controller, demonstrating superior efficacy in regulating the system dynamics. To verify the efficacy of the proposed control strategy, a thorough comparison is done using control simulations between it and previous PID systems. The results show a 31% decrease in speed overshoot. [ABSTRACT FROM AUTHOR]

In 5G networks and beyond, managing handovers (HOs) becomes complex because of frequent user transitions through small coverage areas. The abundance of small cells (SCs) also complicates HO decisions, potentially leading to inefficient resource utilization. To optimize this process, we propose an intelligent algorithm based on a method that utilizes a fuzzy logic controller (FLC), leveraging prior expertise to dynamically adjust the time-to-trigger (TTT), and handover margin (HOM) in a 5G ultra-dense SC heterogeneous network (HetNet). FLC refines TTT based on the user's velocity to improve the response to movement. Simultaneously, it adapts HOM by considering inputs such as the reference signal received power (RSRP), user equipment (UE) speed, and cell load. The proposed approach enhances HO decisions, thereby improving the overall system performance. Evaluation using metrics such as handover rate (HOR), handover failure (HOF), radio link failure (RLF), and handover ping-pong (HOPP) demonstrate the superiority of the proposed algorithm over existing approaches. [ABSTRACT FROM AUTHOR]

This work presents a comparative study of a step-down converter controlled through the algorithm MPPT Perturb and Observe (P&O) with or without a fuzzy logic controller supplied by a photovoltaic system. This study aimed at increasing the quantity of electric energy taken over from the photovoltaic systems by the load through the DC-DC convertor. To follow up the maximum power point where the transfer is performed from the photovoltaic system to the load at maximum power, the Perturb and Observe (P&O) method was used. Two programs were elaborated in MATLAB-Simulink R2018a to control the buck convertor commanded through the P&O algorithm with or without a fuzzy logic controller. The analysis of the results showed that a higher quantity of energy is transferred from the source to the receptor circuit in the case of the buck convertor controlled through the P&O algorithm with a fuzzy logic controller. The P&O algorithm was implemented on an experimental stand at the Laboratory of Electrical Machinery and Drives of the Engineering Faculty in Bacau, with the help of a program issued in the Arduino IDE programming environment. The analysis of the results showed that for an increase in the power conveyed to the receptor circuit, there will also be an increase in the filling factor of the PWM signal that controls the power transistor in the structure of the DC-DC convertor. The P&O algorithm with a fuzzy logic controller may also be implemented in the DC-DC converters in the structure of the driving systems of electric vehicles. [ABSTRACT FROM AUTHOR]

Water is essential for many agricultural and human needs. The use of fossil fuels in water pumping systems has an effect on the environment. A new energy paradigm is being adopted as part of the sustainable development goals, and carbon-free technologies are being widely used to generate renewable energy. This paper presents a fuzzy-based maximum power point tracking (MPPT) approach for a photovoltaic (PV) water pumping system that employs particle swarm optimization (PSO). Additionally, the fuzzy logic control (FLC) scheme for power converters was used in SIMULINK/MATLAB to design and simulate the MPPT of the PV system. The FLC inputs and output scaling gains were adjusted using the PSO algorithm. In addition, a comparative evaluation of the performance of different MPPT controllers was carried out. It made use of fuzzy logic, a PSO-based fuzzy controller, and the perturb and observe technique. According to the simulation results, the simulated photovoltaic water pumping application has high efficiency levels of a normal fuzzy logic, a PSObased fuzzy controller, and the perturb and observe technique are 95.65%, 96.5%, 84.99%, respectively. The results further indicate that the overall efficiency of the PV water pumping system can be significantly increased by using the recommended PSObased fuzzy controller technique. [ABSTRACT FROM AUTHOR]

Abstract Transfer of laboratory-scale experiments to production-scale ethanol fermentation is time-consuming and involves expensive prototype systems from complex experimental designs that determine optimal operating conditions for minimal substrate and product inhibitions. The study developed and validated a Simulink-based model for optimal pH and temperature control using fuzzy logic and PID controllers respectively and taking advantage of 2D and 1D substrate and product inhibition models from which suitable ethanol fermentation reaction rates models were selected. Temperature and pH levels and substrate, product, and biomass concentrations were measured. Selected inhibition models were linear-product, linear substrate-sudden stop product, and linear substrate for cassava, maize, and sorghum, respectively. Fuzzy logic controller ascertained optimal flow rate of acid and base as 0.000196 ml/s and 0.000204 ml/s, respectively, and pH error and rate of pH error as 0.00334 and 0.00368, respectively. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1905; maize: F critical 0.9704, F calculated = 0.2149; sorghum: F critical = 0.9704, F calculated = 0.2488). PID logic controller showed model curves and experimental curves with good fit. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1288; maize: F critical = 0.9704, F calculated = 0.2083; sorghum: F critical = 0.9704, F calculated = 0.2016). The study provided an improved approach as solution for optimal pH and temperature conditions in order to mitigate substrate and product inhibitions during ethanol fermentation. It illustrated that the application of artificial intelligence-based controllers provides satisfactory outcomes that are desirable for implementation in the industrial space. Graphical Abstract

This research paper proposes a hybrid fuzzy logic controller for achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm. The proposed controller employs kinematic modelling to determine the mobile robot’s path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controller, while the FLC is responsible for obstacle avoidance. The effectiveness of the proposed controller has been analyzed, and a comparative study has been carried out with optimization techniques like particle swarm optimization (PSO) and cuckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) and the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO. With the SSO-based FLC, the proposed mobile robot identifies the obstacle distance and travels towards the destination with smooth navigation. The results show the efficacy of the proposed controller in comparison to other controllers for mobile robots in terms of path navigation and obstacle avoidance.

Intelligent control systems developed for production facilities significantly contribute to production efficiency and quality. Using intelligent control systems has now become a necessity in iron and steel sintering plants that produce millions of tonnes annually. Automatic control of the sinter machine speed, which directly affects production efficiency and quality, is one of the first issues to be addressed. The complexity of the sintering process, being affected by many variables, and the nonlinearity of these variables make it difficult to control the machine speed. This study demonstrates that we have overcome this challenge using a fuzzy logic controller (FLC), which is optimized with an adaptive neuro-fuzzy inference system (ANFIS). The FLC we have designed operates with the characteristic point of the thermal state, the mixture level, the vacuum average, and the current speed parameters. We achieved an average success rate of 95%. The developed system automatically controls the speed of the sinter machine with high accuracy, independent of the operator. The system we have developed is used continuously at the Iskenderun Iron & Steel Co. sinter plant. The results obtained from the production facility show that the developed system captures the thermal change in the sinter pallet and manages the machine accordingly, increases the sintering efficiency by at least 10%, and ensures process safety. These results revealed that the developed system can be used effectively in the iron and steel industry and the use of the system will increase efficiency. [ABSTRACT FROM AUTHOR]

A wheelchair is the primary means of mobility for individuals unable to walk. This study aimed to develop a safety system for electric wheelchairs to help people with tetraplegia avoid obstructions. The main contribution of this study is the implementation of a sensor with a wider reflection angle and the adjustment of the wheelchair's speed based on the distance to the obstruction, eliminating the need for manual speed selection. The safety system utilizes LV-MaxSonarEZ1 ultrasonic sensors, which function as reflectance distance readers placed on the front, rear, right, and left sides of the wheelchair. The output from the sensors is input into an Arduino, which functions as the controller. The safety system employs adaptive speed control based on distance through a fuzzy logic controller. The wheelchair was tested with obstruction distances of 1, 1.8, 3, and 10 m. The wheelchair could stop at a distance of 34.06 cm for forward movement and 45.16 cm for reverse movement. The results of this study successfully demonstrate the creation of a safety system on a wheelchair using ultrasonic sensors to avoid obstructions and detect areas, with more adaptive speed control based on distance through a fuzzy logic controller. [ABSTRACT FROM AUTHOR]

The concept of digital farming can help farmers increase their agricultural production yield. One of the technologies to support digital farming is robotics, which can be utilized to complete a redundant task efficiently for 24 hours. This paper presents a simple and effective harvesting robot that is applied to harvest a ripe strawberry. The mechanical and electrical design is kept simple to ensure it is reproducible. The input from a proximity sensor and image detection by a Pi camera is utilized by FLC (Fuzzy Logic Controller) to improve the effectiveness of the harvesting task. The image processing method in this study is image segmentation, which fits with the limited source of the microcontroller available in the market. The experiment included 60 times (20 times center, left, and right position) harvesting using the FLC algorithm and 60 times without FLC to show the effectiveness of the proposed method. From 60 experiments without an FLC experiment, there is an 80% hit rate for strawberries positioned in the middle of an image plane and 55% for left and right strawberries. From 60 times of FLC experiment, 95% hit rate for strawberries positioned in the middle of an image plane, 80% for left and right strawberries. The average time required to finish the task without FLC for strawberries in the middle is 13.51 s, the left is 11.04 s, and the right is 17.28 s. While the average time required to finish the task with FLC for strawberry in the middle is 12.90 s, the left side is 11.71 s, and the right side is 10.93 s. This study is intended to show that simple designs can be helpful and affordable when applied to greenhouse farming in Indonesia.

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.

This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller's robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) have been broadly applied in various fields, such as medicine and agriculture. A network is likely to experience information congestion when many sensors initiate sending data concurrently. This could lead to maximizing in the packet loss ratio, thereby reducing efficiency and affecting the total system performance, so congestion control is an utmost challenge. To solve this problem, a Fuzzy Congestion Control in WSN based on the Spider Monkey Optimization Algorithm (FCC-WSN-SMOA) is proposed. The proposed method combines random early detection with the fuzzy proportional integral derivative controller. The proportional integral derivative (PID) and fuzzy logic conjointly together to help control the target buffer queue. FLC regulates the transmitting rate of every node. Then, FLC input and output parameters are optimized by SMOA. The simulation is activated in MATLAB. The FCC-WSN-SMOA method attains 21.28%, 32.20%, and 17.42% lower packet loss ratio and 16.25%, 26.07%, and 23.38% lower packet loss probability compared with existing methods, such as progressive fuzzy PSO-PID congestion control approach for WSN (FCC-WSN-PSO), optimized fuzzy clustering utilizing moth-flame optimization approach in WSNs (FCC-WSN-MFOA), and time synchronization depending on Improved Wolf Colony Algorithm-Cuckoo Search Optimized Fuzzy PID Controller for Smart Grid (FCC-WSN-IWCA-CSO), respectively. [ABSTRACT FROM AUTHOR]

This paper critically reviews various energy-saving models used for enhancing the energy efficiency of hydrostatic power transmission drives. The system layout, component design, and controllers used are discussed in detail in its sub-category. Based on the detailed review of various energy-saving models, a novel energy-efficient hydrostatic power transmission drive of the chairlift transport system (CTS) is proposed. The rise in fluid temperature in the existing hydraulic arrangement of the CTS, owing to its continuous recirculation, is controlled by incorporating the flushing valve, while the boost pump replenishes the fluid volume loss. However, because of its loading nature, the CTS's existing power transmission drive encounters energy losses and uncontrolled fluid cooling. In the present study, the hydraulic circuit design of the existing power transmission drive of the CTS is modified to reduce energy losses. Using a variable displacement auxiliary pump in the system helps minimize the energy losses through the pressure relief valve. Also, the proposed design enhances the cooling efficiency of the flushing valve unit controlled through a suitably designed Fuzzy logic controller having a dependency on fluid temperature. The simulation model is made in MATLAB/ Simulink® environment, and the result shows that, with the increase in speed, the orifice area opening of the proportional flow control valve increases to prevent oil temperature from exceeding the operating range and the same decreases for a reduced speed. Furthermore, the proposed system exhibits a significant energy saving of 32 kJ over 300 s, demonstrating its improved efficiency compared to the existing system. [ABSTRACT FROM AUTHOR]

This research introduces an innovative protection methodology designed to discriminate between disturbance events and asymmetrical/symmetrical faults within a microgrid (MG). The proposed methodology integrates a detection method utilizing covariance analysis of wavelet transform coupled with the sum of absolute differences approach and a fault classification method based on a fuzzy logic controller (FLC). The methodology leverages the advantages of covariance analysis, facilitating a straightforward demonstration of fault presence through the relationships among phases. In addition, a well-defined procedure effectively distinguishes between disturbance and asymmetrical/symmetrical events, accurately identifying the faulted phase(s) through FLC. The efficiency of the presented protection methodology is further enhanced when complemented with the identification of disturbance events. A comprehensive evaluation is conducted under diverse conditions, including variations in fault resistance, fault inception angle, and fault location. Furthermore, the methodology's performance is validated through the consideration of fluctuating solar irradiance levels and source strength variations. To verify the robustness and applicability of the proposed protection methodology, various scenarios involving an unbalanced network model, nonlinear load, uncertain parameter variations, and measurement noise are systematically addressed. The methodology's effectiveness has been demonstrated through extensive evaluation on grid-connected MG with both radial and mesh topologies. Finally, the application of this protection methodology to islanded MG is discussed, exploring potential effects and highlighting the versatility of the proposed approach. This study improves MG protection strategies by emphasizing the adaptability and efficacy of the proposed methodology in a variety of operational scenarios. [ABSTRACT FROM AUTHOR]

For regulating the Synchronous Generator (SG) output voltage, the Automatic Voltage Regulator (AVR) system is a significant device. This work propounds a survey on Optimization Algorithms (OAs) utilized for tuning the controller parameters on the AVR system. A device wielded for adjusting the SG's Terminal Voltage (TV) is named AVR. A Controller is utilized for improving stability and getting a superior response by mitigating maximum Over Shoot (OS), reducing Rise Time (RT), reducing Settling Time (ST), and enhancing Steady State Error (SSE) since output voltage has a slower response and instability. The controllers utilized here are Proportional-Integral-Derivative (PID), Intelligent Controller (IC), along with Fraction Order PID (FOPID). Owing to the occurrence of time delays, nonlinear loads, variable operating points, and others, OAs are wielded for tuning the controller. (a) Particle Swarm Optimization (PSO), (b) Genetic Algorithm (GA), (c) Gray Wolf Optimizer (GWO), (d) Harmony Search Algorithm (HSA), (e) Artificial Bee Colony (ABC), (f) Teaching Learned Based Optimization (TLBO), et cetera are the various sorts of OA. For enhancing the TV response along with stability, various OAs were tried by researchers. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have become a vital solution for environmental transportation; however, challenges related to battery life and power density persist. In pursuit of enhanced EV performance and cost-effectiveness, researchers advocate for Hybrid Energy Storage Systems (HESS), integrating various Energy Storage Systems (ESS). An efficient Energy Management Strategy (EMS) is crucial for optimal power distribution within the HESS. This study introduces a real-time, simple, and practical EMS using a low-pass filter (LPF). However, the LPF lacks State of Charge (SoC) control, necessitating the addition of a SoC Limiter. The static SoC Limiter, while effective, faces challenges in predicting peak loads, leading to suboptimal power-sharing performance. To address this limitation, LPF with Adaptive SoC Limiter (LPF-ASL) is proposed. The LPF-ASL accommodates the peak load by saving some portion of supercapacitor (SC) power for peak load. In an unpredictable initial SC SoC test, LPF-ASL achieves substantial reductions in maximum battery current compared to LPF and Fuzzy Logic Control (FLC) by up to 21.30% and 21.14%, respectively. This underscores the effectiveness of LPF-ASL in optimizing battery life and enhancing power distribution within HESS-equipped EVs. [ABSTRACT FROM AUTHOR]

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero. [ABSTRACT FROM AUTHOR]

This study examines the design and performance comparison of Proportional-Integral (PI) and Fuzzy Logic Controllers (FLC) for a Dual Active Bridge (DAB) converter, specifically within the framework of solar-integrated off-board fast charging for electric vehicles (EVs). The main goal is to create and assess control strategies that facilitate efficient and stable power transfer between the PV System and the EV battery, effectively managing the challenges associated with fluctuating solar irradiance and changing battery load conditions. The approach includes designing both PI and FLC controllers specifically for the DAB converter and then conducting a simulation-based assessment across various operating conditions. The PI controller is adjusted using traditional tuning methods, whereas the FLC is crafted to handle the system's nonlinearities. The evaluation focuses on key performance indicators such as voltage regulation, response time, and overall system performance. The results indicate that although PI controller performs adequately in steady-state scenarios, the FLC outperforms it in terms of dynamic response, especially when managing disturbances and system uncertainties. Additionally, the FLC improves the overall efficiency of the solar-powered charging system by optimizing power flow amidst fluctuating solar input and load conditions. The innovation of this study is rooted in applying a FLC to a DAB converter within the unique setting of solar-integrated fast charging for EVs, an area that appears underexplored in current literature. The proposed FLC provides a strong solution to the challenges of incorporating renewable energy into fast EV charging systems, contributing to the development of more sustainable and dependable charging infrastructure. [ABSTRACT FROM AUTHOR]

Control technologies are of great importance in the rapidly developing electric vehicle industry. Control systems, especially in electric vehicles with more than one drive system or energy source, are at the top of the issues focused on to achieve low fuel and emission values, to provide adequate performance, and not to compromise on safety. Fuzzy logic controller (FLC) based systems are among the most preferred control systems with their adjustable, adaptable features, flexibility, and robust structure against uncertainty. Control systems developed using FLC are divided into sub-branches of propulsion, energy source, vehicle dynamic system, and environmental effects. In this review study, after revealing what needs to be done about the control system in these branches, it is explained why fuzzy logic is preferred in the solution of these problems and the studies done with FLC are summarized. It has been emphasized that fuzzy logic can be a leading solution tool in cases where the mathematical model cannot be presented. In addition, attention was drawn to optimization algorithms that provide the most accurate parameters to operate a fuzzy system in the most appropriate way, and examples of this were given. [ABSTRACT FROM AUTHOR]

The quadcopter persist as important roles across diverse applications, and the enhancement of their control efficacy has been the subject of extensive research. In this work, the authors proposed optimal Proportional Integral (PI) controller based Fuzzy Logic Control (FLC) for the roll, pitch, altitude, and yaw motions of the quadcopter system. The proposed technique uses the Particle Swarm Optimization (PSO) algorithm to tune the parameters of the FLC and enhance the quadcopter performance. The simulation results show that the proposed technique achieves smoothness of control and significant improvement over classical techniques, as the rise time and the settling time are reduced by 61 % and 66 %, respectively. These times are important for stabilizing the system's response speed and avoiding overshooting or oscillating. This indicates that the FLCPI-PSO can achieve the desired roll and altitude angles more rapidly and effectively. [ABSTRACT FROM AUTHOR]

A fuzzy logic control strategy for temperature and relative humidity control of air-handling unit in heating, ventilating, and air-conditioning systems is proposed. Those systems operating in the cooling process are multi-variable and nonlinear systems and their control is very difficult. The dynamics of all elements of the system are presented and the case of an unknown external disturbance is also considered. The zone temperature is regulated by controlling the flow rate of cold water passing through the cooling coil. The numerical results for the uncontrolled, On–Off controlled, and Proportional-Integral-Derivative (PID) controlled systems are compared with the proposed fuzzy logic controlled system under the weather conditions of Istanbul, Turkey in summer season. From the numerical results, it was observed that for the zone temperature the 78 min settling time of the uncontrolled case was reduced to the 26 min by the proposed fuzzy logic controller, which yielded the best result among the other controllers. The PID and fuzzy logic controllers provided smoother control signals when compared to the fluctuating control signal of the On–Off controller for the flow rate of cold water. The energy consumptions of the controllers, normalized with respect to the uncontrolled case, were also compared to show the expense of achieving fast time response. The increase is only between 2.5–7.4% and the proposed fuzzy logic controller used less energy than the PID controller. [ABSTRACT FROM AUTHOR]

The manuscript proposes the design and implementation of a photovoltaic-assisted dynamic voltage restorer with fuzzy-logic control (FLC)-based Improved Second-order Generalized Integrator (I-SOGI) scheme to improve power quality (PQ) in the power system by using an enhanced approach. The proposed method utilizes Firebug Swarm Optimization (FSO) which is motivated by the reproductive swarming behavior of Firebugs. The swarming behavior of FSO is enhanced by using two operators called mutation and crossover. Hence, it is termed as Enhanced FSO (En-FSO) method. Power quality issues considered include harmonic distortions and voltage sag/swell. The Z-source inverter (ZSI) by compensating the dynamic voltage restorer (DVR's), fed voltage into the grid through a series transformer. Long-term balanced voltage sags, swells, unbalanced sags, swells, and interruptions of voltage are all offset by the ZSI-DVR. Using the suggested control method, the total harmonic distortion (THD) of load voltage is kept below 5%. The FLC-based I-SOGI control method for generating the reference signals for voltage compensation enhances a considerable parameter tweaking. The entire analysis demonstrates that the proposed FLC-based I-SOGI control technique effectively mitigates voltage sag, swell, and harmonics while supplying solar photovoltaic (SPV) power to the grid. The simulation and experimental verification of the proposed control algorithm successfully maintains pure sinusoidal load voltage in a variety of test conditions. The results are compared with existing methods to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Effective management of blood glucose levels in individuals with type 1 diabetes, especially after meals, is crucial for diabetes care. Artificial pancreas systems (APS) perform automated insulin delivery in subjects with type 1 diabetes mellitus (T1DM). In this study, an optimized fuzzy logic controller was designed to achieve a euglycemic range after a substantial meal intake. All in silico simulations were performed using the MATLAB/Simulink environment, leveraging control variability grid analysis (CVGA), and the performance of the controller was evaluated. The proposed controller is based on a fuzzy-logic control law designed in three stages. First, a nonlinear framework of the glucose-insulin regulatory system was identified based on the heavy meal protocol of three patients given as follows: for subject ID 117-1, a total of 295 gCHO; for subject ID 126-1, 236 gCHO; and subject ID 128-1, 394 gCHO over a day. Then, an iterative tree structure was employed to establish a stabilizing control rule for insulin delivery, integrating inputs from two Mamdani Fuzzy Inference System (FIS) objects. Finally, a genetic algorithm refines the control system by fine-tuning the uncertainty of the fuzzy membership functions. Two scenarios were considered for three patients to assess the performance of the proposed controller. The results indicated its effectiveness under various conditions, achieving a time in the range of 61.25%, 71% and 61.10% respectively for the three subjects. The obtained results are analyzed and compared with IMC and multi-objective output feedback controllers. The findings of the study reveal that the proposed controller shows promising advancements in tailored strategies for type 1 diabetes patients, outperforming the other controllers in terms of blood glucose regulation. [ABSTRACT FROM AUTHOR]

Articulated heavy vehicles (AHVs) play a vital role in the economy of freight transport. Lateral stability control of AHVs during the immediate vicinity to loss of control (LOC) is a significant issue that has not been effectively addressed in the literature. This paper presents a novel approach to the lateral stability control of tractor semi-trailers under conditions leading to LOC. An active auxiliary axle is proposed to prevent trailer swing and snaking during severe lane change maneuvers. A linear 3-DOF model was developed to represent the effectiveness of the active auxiliary axle and tire cornering stiffness in yaw-rate control to provide an analytical basis. Nonlinear modeling of the axle and vehicle system was performed in TruckSIM. A Fuzzy Logic Controller (FLC) was developed to identify the required rate (magnitude per unit time) of actuation force, and a PID controller was introduced to regulate the magnitude of actuation force at the axle-wheel interface. Co-simulation was performed in MATLAB/SIMULINK in combination with TruckSIM. To simulate an LOC on a dry road with a coefficient of friction of 0.85, a double lane change (DLC) maneuver at 90 km/h was conducted. This resulted in a combined state of relative roll-over and trailer swing, facilitating the evaluation of the semi-active auxiliary axle's performance in regaining stability and eliminating transient overshoots in the vehicle combination's lateral response. Yaw rate rearward amplification was effectively controlled, and articulation angle oscillations were significantly diminished. This approach suggests a systematically minimal yet practicable retrofit to the trailer, contributing to a remarkable improvement in the traffic safety of AHVs. [ABSTRACT FROM AUTHOR]

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This study presents a pioneering methodology for implementing the maximum power point tracking (MPPT) controller, based on fuzzy logic. Through a comprehensive performance analysis, we evaluate its effectiveness compared to the widely used perturb and observe (P&O) algorithm, which is a common MPPT technique. The main objective of our proposed MPPT approach is to improve the performance of a photovoltaic (PV) system. To evaluate the performance of the proposed MPPT controller and compare it with the P&O algorithm, we designed and simulated both controllers using MATLAB/Simulink. We also implemented a prototype of the controllers using an Arduino Mega board, and evaluated their performance under real operating conditions. The experimental results unequivocally confirm that the fuzzy logic-based MPPT controller outperforms the P&O algorithm in terms of performance, speed and accuracy. The fuzzy logic controller offers greater accuracy in tracking the maximum power point under various environmental circumstances, including variations in solar irradiation and connected load. Overall, this work contributes to the development of efficient and reliable MPPT controllers for PV systems, and provides a comparison of the performances of two popular MPPT techniques. Future research could explore other MPPT techniques and evaluate their performance using similar experimental setups. [ABSTRACT FROM AUTHOR]

The power generated by photovoltaic (PV) systems is influenced by environmental factors. This variability hampers the control and utilization of solar cells' peak output. In this study, a single-stage grid-connected PV system is designed to enhance power quality. Our approach employs fuzzy logic in the direct power control (DPC) of a three-phase voltage source inverter (VSI), enabling seamless integration of the PV connected to the grid. Additionally, a fuzzy logic-based maximum power point tracking (MPPT) controller is adopted, which outperforms traditional methods like incremental conductance (INC) in enhancing solar cell efficiency and minimizing the response time. Moreover, the inverter's real-time active and reactive power is directly managed to achieve a unity power factor (UPF). The system's performance is assessed through MATLAB/Simulink implementation, showing marked improvement over conventional methods, particularly in steady-state and varying weather conditions. For solar irradiances of 500 and 1,000 W/m2, the results show that the proposed method reduces the total harmonic distortion (THD) of the injected current to the grid by approximately 46% and 38% compared to conventional methods, respectively. Furthermore, we compare the simulation results with IEEE standards to evaluate the system's grid compatibility. [ABSTRACT FROM AUTHOR]

Photovoltaic systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The power output of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. To optimize the generation of electricity from solar systems, the maximum power point tracking (MPPT) technique is employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb and observe (P&O), have been modified. This research paper aims to simulate and validate the step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using MATLAB/Simulink for efficient power tracking in photovoltaic systems. [ABSTRACT FROM AUTHOR]

Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal databases, followed by an evaluation of target articles based on predefined criteria. The information was then classified into seven categories, including control system type, features of the fuzzy logic controller, fuzzy logic inference strategy, power quality issue, control device, implementation methodology (efficacy testing), and efficiency improvement. Our study revealed that fuzzy-based control systems have evolved from simple type-1 fuzzy controllers to advanced control systems (type-2 fuzzy and hybrid) capable of effectively addressing complex power quality issues. We believe that the insights gained from this study will be useful to both experienced and inexperienced researchers and industry engineers seeking to leverage fuzzy logic to enhance power quality control. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to power conversion in electric vehicle (EV) applications. The proposed converter, a Bridgeless Single Stage Positive Luo Converter (BSPLC), is optimized to enhance efficiency and reduce losses in low-capacity EVs. Traditional converters experience higher losses due to their passive components and bridge circuits. By eliminating the bridge components, the converter achieves higher efficiency. An intelligent fuzzy logic controller is employed to provide adaptive control and stabilize output under varying input conditions, improving performance and response time. The converter design is further optimized through parameter tuning and simulation to achieve minimal ripple and maximum power efficiency at 92%. The proposed solution is ideal for low-capacity EVs, as it ensures enhanced power conversion, reduced thermal stress, and improved battery life. The study demonstrates the converter’s capability to meet the growing demands for energy-efficient solutions in modern electric mobility.

The use of electronic load controllers (ELCrs) is widely adopted in pico hydropower systems to maintain output power supplied to the consumer load, regardless of changes in consumer demand. This is due to the absence of moving mechanical parts, affordability, prevention of the hammer effect in pipes, and being more efficient than the governor systems. However, implementing existing ELCrs in a pico hydropower system can pose challenges related to power quality, efficiency, or costs. In this paper, a fuzzy PI-based single-switch bidirectional AC chopper electronic load controller (FP-SSBAC ELCr) is proposed. This configuration reduces the number of insulated gate bipolar transistors (IGBTs) from two, typically found in the conventional bidirectional AC choppers, to one per phase, resulting in cost reduction. A hybrid controller, comprising fuzzy and PI controllers, is designed to quickly maintain a constant output voltage and frequency when consumer load abruptly changes. The gains of the PI controller are updated by the fuzzy logic controller based on the voltage error and its derivative. The proposed model is simulated in MATLAB/Simulink and validated experimentally under sudden changes in consumer load. The results achieved with the FP-SSBAC ELCr demonstrate improved dynamic performance without overshoot compared to PI-based ELCrs. The highest recorded voltage and current total harmonic distortions (THDs) are 2.8 % and 2.1 %, respectively, meeting the IEEE 519 standard. Therefore, the proposed model has the potential to enhance performance and efficiency and can be implemented cost-effectively in pico hydropower systems.

This paper presents a comprehensive study on advanced control and energy management in a multi-source/multi-load microgrid. The microgrid integrates solar panels, a wind turbine system, a Li-ion battery-based storage system, an electric vehicle (EV), and a DC load, all inter-connected through power electronic converters to a DC bus and linked to the AC grid via a DC/AC inverter. The study outlines the primary control objectives: DC bus voltage regulation, optimization of photovoltaic and wind energy conversion, and maintaining high-quality energy injection into the grid. Given the intermittent nature of solar and wind energy and the varying energy demands that affect battery life and performance, a novel energy management algorithm is introduced. This algorithm addresses different operating modes, such as Constant Current (CC) and Constant Voltage (CV), to enhance network stability, energy quality, and optimize energy distribution between the various sources and the EV. The proposed fuzzy logic controllers, known for their robustness to uncertainties, flexibility, and ease of implementation, are employed to achieve these objectives. The Particle Swarm Optimization (PSO) is used to fine-tune the fuzzy logic control parameters, ensuring optimal performance. This optimization process included minimizing Integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Square Error (ITSE) as objective functions. Extensive simulations under various operating scenarios demonstrated significant improvements in control accuracy, stability, and system performance.

The energy consumed by artificial lighting accounts for a significant part of the energy consumption in buildings. Thus, energy consumption can be significantly decreased using optimization techniques, such as the optimum placement of the lighting sensors. In this paper, an optimal placement method of the light sensors is presented to minimize the lighting system's equipment costs and energy consumption and also meet visual comfort under the requirements of the European standard EN12464–1 regarding illuminance-based criteria. In the beginning, based on the illumination measurement grid's mathematical model, the potential location of the sensors is determined. As an average of the dimming levels of the LED lights, the objective function has been introduced. Then, the illuminance and uniformity levels are defined as constraints for the optimization problem. The Battle Royale Optimization (BRO) algorithm is used to solve the optimization problem. Ultimately, based on the results of the BRO algorithm and the calculation of the illuminance deviation, the optimal location of the light sensors is determined. The proposed method is tested in an office room. A fuzzy logic controller is developed to regulate the lighting control system's dimming levels to evaluate the proposed method's performance with other approaches. The comparison results have shown that the proposed method is superior as regards the number of sensors and the optimal sensor position, significant savings in energy consumption of up to 30.8%, and satisfactory visual comfort per the requirements of the European standard EN12464–1.