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Low inertia poses a significant challenge to the widespread integration of photovoltaic (PV) systems into the grid, particularly affecting frequency stability. This paper addresses this challenge by proposing an advanced hybrid inertia (AHI) approach, characterized by the integration of real and virtual inertia, combined with the application of intelligent control techniques, to enhance the primary frequency response of a PV system. Internal inertia is provided by a synchronous generator (SG) acting as a compensator, while virtual inertia is generated through a real-time deloading strategy in the PV plant. The AHI is implemented on an isolated grid, utilizing various intelligent control techniques to improve frequency stability. The intelligent techniques employed include genetic algorithms (GA) and fuzzy logic controllers (FLC), which do not require mathematical modeling. This allows them to overcome the nonlinear dynamics of the PV plant and the uncertainties associated with climatic changes. These techniques are applied to the nonlinear components on the generator side, including the governor loop, DC-DC converter loop, and power reserve injection management loop. A frequency fluctuation scenario, induced by generating power perturbations in the proposed system, demonstrates and validates the improvement in frequency stability achieved by the HI and intelligent techniques. All tests are conducted using MATLAB Simulink. Various simulation results show a significant improvement in frequency stability through the incorporation of HI and intelligent control techniques. [ABSTRACT FROM AUTHOR]

Internal combustion engine (ICE) vehicles often exhibit quicker acceleration and a faster response to reach maximum speed than electric vehicles (EVs). The EVs can overcome this limitation with highly efficient motors and optimized control systems. This work proposes a speed control strategy for field-oriented control (FOC) PMSM-based battery electric vehicles (BEV) using a fuzzy logic controller (FLC) to attain quick acceleration and smooth steady-state operation. A minimal set of fuzzy rules is framed to achieve precise speed control of the PMSM propulsion system. A mathematical model is established in MATLAB-Simulink to emulate a BEV with a battery-driven PMSM propulsion system and a single-gear transmission to the wheels, including vehicle dynamics. The performance of this system is comprehensively evaluated during transient and steady-state conditions, focusing on parameters such as rise time, settling time, and steady-state error in operating speed and torque characteristics of the PMSM-BEV. Furthermore, the acceleration of the BEV at the wheels is assessed in terms of the time required to reach its maximum speed, typically 100 kilometers per hour. The results of the simulations are contrasted with conventional P.I. and P.I.D. controllers. The findings from these results validate that the minimal fuzzy rulesbased BEV is more resilient than P.I. and P.I.D. counterparts for quick acceleration and smooth steady-state operation, which is more suitable for electric automobiles

Renewable energy promises to be a good substitute for traditional sources for a constant supply of power. Large-scale electrical grids may experience major synchronization imbalances between various components as a result of system or communication delays. Engineers experience several challenges while attempting to replace conventional energy with sustainable power since the characteristics of renewable power plant generation are continually changing with climatic conditions. To manage a time-delayed automated generation control (AGC) system, one can use intelligent control such as the FUZZY-assisted three degrees of freedom PID controller. This study presents a new control strategy called the FUZZY-3DOF-PID controller for a First-Order Plus Time-Delay (FOPTD)-based three-area interconnected hybrid power system. The controller parameters are trained by different algorithms, such as FireBug Swarm Optimization (FSO), Levenberg Marquardt Algorithm (LMA), Particle Swarm Optimization (PSO), and Big Bang Big Crunch (BB-BC) Algorithm, to prove their efficacy. The designed controller is robust to load variation (RLP) and the comparison of the various performance indices demonstrates the superiority of the proposed controller over other controllers available in the literature. [ABSTRACT FROM AUTHOR]

This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module's operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios. [ABSTRACT FROM AUTHOR]

Two parameters, voltage and frequency, are required for smooth delivery of quality power to consumer. Therefore, in this paper enhancement of frequency and voltage stability of multi microgrid system with a proposed hybrid fuzzy logic controller with two-degree of freedom tilt-integral derivative with filter (2DOFTIDFFUZZY) are analyzed. A novel algorithm known as artificial electric field algorithm (AEFA) technique is employed to obtain optimal controller parameters. Firstly, a two-area system widely used in literature is considered for proper comparison. Efficacy of 2DOFTIDFFUZZY controller is confirmed by comparing it with different controllers for the same system. Further, a multi microgrid system is considered for unified voltage-frequency control. Automatic Voltage Regulator (AVR) effect states a substantial improvement in system dynamics with the proposed controller. Under different unfavorable conditions like source and load variation, dynamic system performances are studied to enlighten robustness of proposed controller. Simulation results elucidate that proposed 2DOFTIDFFUZZY controller optimized with AEFA technique can achieve better system stability in case of any vulnerability. [ABSTRACT FROM AUTHOR]

This research focuses on the development and evaluation of global maximum power point tracking MPPT controllers for enhancing the dynamic performance of Photovoltaic (PV) systems under partial shading conditions. Three different controllers, namely Proportional-Integral (PI), Fractional-Order PI (FOPI), and Fuzzy Logic Controllers (FLC), are studied in conjunction with a modified Incremental Conductance (IC) and Fractional Open-Circuit Voltage (FOCV) algorithms. The proposed controllers with a novel optimization algorithm called Atom Search Optimization Algorithm (ASOA) are compared with classical control. The objective is to investigate the effectiveness of the global MPPT controllers in achieving higher tracking accuracy, faster convergence, and improved stability under varying shading conditions. The study involves extensive simulations using a representative PV system model subjected to different shading scenarios. The results demonstrate that the proposed global MPPT controllers with the ASOA algorithm, when integrated with the modified MPPT algorithms, outperform the classical control techniques in terms of dynamic performance and response to partial shading conditions. The suggested ASOA-based fuzzy IC MPPT controllers can reach the MPP more quickly with a shorter settling time and a better tracking response. Furthermore, with a reduced settling time of about 1.9 % and an average power harvesting efficiency of over 97.9 %, ASOA-based fuzzy IC MPPT controllers offer the best harvesting characteristics. The controller exhibits superior tracking accuracy and stability, mitigating the effects of shading-induced multiple peaks in the P-V curve.

This paper purposes to demonstrate the effectiveness of fuzzy logic controller (FLC) over proportional integral (PI) controller for reducing the fault current and maintaining the voltage profile at different faults conditions using Static Synchronous Compensator (STATCOM) which is considered an effective FACTS (Flexible Alternating Current Transmission System) device. The study evaluates the performance of a power system equipped with STATCOM which is connected in shunt with bus B1 under various faults conditions, including single-phase and three-phase faults. The performance of the STATCOM is evaluated by using two different controllers: PI controllers and FLCs. A comparative analysis is done between the performances of the two different controllers using Matlab/Simulink software package. The results obtained conclude that the presented system gives better performance with STATCOM as compared to not using it under several faults conditions besides, the STATCOM gives better response with FLC as compared to PI controller. It is demonstrated that STATCOM with FLC can reduce the positive sequence fault current at bus B1 to 96.49% of its value without using STATCOM under line to ground fault and 98.17% under three line to ground fault whereas STATCOM with PI controller can reduce it to (99.57, 99.05%), respectively. Also, the bus voltage B1 is improved to 102.19% by using STATCOM with fuzzy controller under line to ground fault and 101.86% under three line to ground fault whereas STATCOM with PI controller can improve it to (100.21, 100.93%), respectively. [ABSTRACT FROM AUTHOR]

A robotic manipulator is a highly nonlinear, coupled system with many inputs and outputs (MIMO). These days, with the development of technology, using robots has become very common in various fields. It is difficult for the control experts to create an effective controller for this system, so it is preferable to design a non-linear controller that gives an accurate, more efficient controller and good results in robustness and uncertainty. Two distinct controllers for a 3-DoF robotic manipulator are developed and evaluated in this research’s context of Industry. During the investigation, two primary control strategies are introduced. One of the first is a fuzzy-immune PID controller that employs joint position error as inputs. A second type is a fuzzy-immune FOPID controller. The PID controller is a particular case of FOPID controller when $\lambda =\mu =1$ . The research underscores the significance of precise parameter calibration and data acquisition in optimizing the performance of a control system. The parameters of controllers are tuned using the clonal selection algorithm. The controllers’ efficacy was verified through quantitative analysis that employed performance indices, including mean square error (MSE). The robot manipulator is constructed with MATLAB. The proposed technique, which combines intelligent control methods, offers a promising hybrid control design. Our software implementation has demonstrated that the fuzzy immune FOPID controller is more efficient in terms of reduced tracking error of the manipulator for its dynamic control in the joint space than other controllers typically used in practice. The positioning control system utilizes Fuzzy Immune PID and FOPID control techniques. All controllers were designed using MATLAB.

Lack of effective control over the power flow is one of the main problems in the Point of Common Coupling (PCC) of the microgrid and the main utility. To tackle this issue, this paper is on developing a confident, simple, and low-cost way for smooth and bidirectional control of power transfer between the microgrid and the main utility. The proposed approach is based on smart transformer (ST) which is an isolated high-frequency power converter that consists high-frequency step-down tap-changer transformer and three-phase to single-phase matrix converters. These converters contribute to improve the microgrid power management during grid-connected mode. For the simultaneous control of voltage and frequency, the method of Dual Virtual Quadrature Source (DVQS) with sinusoidal duty cycle is proposed to effectively adjust the switching parameters during the fault between the microgrid and the main utility. Moreover, fuzzy logic contributes to a flexible control on active and reactive power flow management. To evaluate the performance of the proposed approach, simulation studies are established on a sample system and discussed in more depth.

This paper proposes a new design methodology to regulate the output voltages of auxiliary power units in an electric vehicle application. The proposed method includes designing and implementing a robust fuzzy logic controller to load variation and input voltage fluctuation. The existing output voltage regulation techniques for auxiliary power units comprise a proportional-integral controller, which fails to provide good transient performance concerning load variation and input voltage fluctuation. To overcome the drawback of the existing technique, thereby improving transient performance, a proportional-integral controller is replaced by a fuzzy logic controller. This paper addresses a detailed analysis of comparing the proportional-integral and fuzzy logic controllers to segregate and utilize the best among them based on their performance. In addition, the issue of cross-regulation is also eliminated for the proposed design. Also, the proposed design involves the use of a single-input triple-output converter of a 100 W prototype which is designed to furnish three output voltages 24 V, 14.4 V, and 5 V to supply the auxiliary power units of an electric vehicle. To validate the effectiveness of the proposed approach, simulation is conducted on the MATLAB/Simulink platform, and the quantitative results of the proposed design, which include output voltage ( $V_{o_{1}}$ ) =24 V, rise time ( $T_{r}$ ) =0.02 sec, and steady-state error ( $e_{ss}$ ) =0, are better than the existing values: output voltage ( $V_{o_{1}}$ ) =23.4 V, rise time ( $T_{r}$ ) =0.345 sec, and steady-state error ( $e_{ss}$ ) =0.6 V.

Maximum power point tracking (MPPT) techniques are regarded as an important component of photovoltaic systems (PVSs) to extract the output power of a photovoltaic array. Algorithms based on perturbation suffer from the tradeoff between steady-state oscillations around (MPP) and the tracking speed. In this article, a fuzzy logic controller (FLC)-based Adaptive P&O MPPT algorithm for PVSs with fast tracking and low oscillations under rapidly irradiance change conditions are developed. When the operation point reaches a steady state, the proposed method can halt any artificial perturbation. Consequently, no oscillations around the MPP, and energy loss is reduced compared with conventional perturbation-based algorithms. The proposed algorithm can detect any change in irradiance without using additional sensors by employing a technique called drift avoidance. In addition, the FLC creates variable step sizes adaptively to accomplish speedy, accurate convergence to the MPP under normal and changeable weather circumstances. In a MATLAB program environment, the suggested method is simulated. DSP TMS320F28335 is used to validate that the proposed algorithm can monitor MPP with a low settling time, low steady-state oscillations, and a high convergence rate at the operational point.

Functional Electrical Stimulation (FES) cycling, or FES-Cycling, holds great therapeutic potential for individuals with paralysis, such as those with Spinal Cord Injury (SCI), traumatic brain injury, or stroke, aiming to restore mobility. However, the nonlinear nature of the musculoskeletal system poses a significant challenge in controlling FES-Cycling. To address this, an integrated closed-loop phase angle fuzzy-based system was developed. This system offers real-time control by adjusting stimulation intensity (pulse width) within the range of 50 to 200μs while maintaining a constant frequency of 35Hz, thereby ensuring precise pedaling trajectory and cadence patterns. An experimental study involved three healthy individuals (Cases A, B, and C) and one individual with hemiplegia stroke (Case D). Results showed that the proposed system consistently reduced average angle trajectory errors for Cases A, B, and C, with values of 2.6945, 3.2958, and 2.9922 degrees, respectively. Case D, affected by hemiplegia stroke, faced greater challenges and exhibited a higher error of 3.4562 degrees. Fatigue resistance, evaluated through fatigue indices, showed promising results for Cases A, B, and C with values of 0.10778, 0.06866, and 0.04603, respectively. However, Case D experienced higher fatigue (0.2304) due to the unique challenges of hemiplegia stroke. These findings highlight the effectiveness of the proposed control system in optimizing FES-Cycling, particularly for healthy individuals. For individuals with paralysis, like Case D, further research is needed to adapt the system to their specific conditions and cycling patterns. This system holds the potential for enhancing FES-Cycling as a therapeutic strategy and warrants additional investigation and customization for different patient populations.

The comparative performance analysis of conventional PI controller and Fuzzy Logic Controller for a switched mode converter application. Switched mode converters needs to regulate its output voltage to lower voltage and must quickly respond to fast load and line variations or transients to keep the output voltage within a limited range. The design of high performance linear proportional integral controller for switched mode converter presents series of challenges due to nonlinear nature of the plant process. Fuzzy logic controller is mostly used in variety of industrial applications including use in domestic appliances and main tool of operation of fuzzy logic controller is membership function. By using the MATLAB and Simulation we compare the characteristics of the linear proportional integral controller and nonlinear fuzzy logic controller with switched mode converter and also which gives the desired results. The main comparison for Proportional Integral controller (PI) and Fuzzy logic controller (FLC) is to get the pure Direct current (DC) without any fluctuations for sensitive applications. The Switched Mode Converter with both the PI controller and Fuzzy logic controller is implemented and output characteristics are analyzed and compared. The switched mode converters are efficient in power conversion, taking a main voltage AC input and converting it down to a low voltage DC output. AC-DC switched-mode converters are better used. It is used for the external desktop power supply laptop, set-top box, and the wall plug-in charger for smartphones and computers. [ABSTRACT FROM AUTHOR]

There is a rapid increase in power demand which has led to an increase in the need for smaller grids all over the world. The microgrid is such a small power supply system that consists of loads, distributed energy resources, and energy storage systems. With the development of the internet, the power sector has started the development of the newest type of grids, involving a smart technology called smart grids. Smart grids have got smart integration and advanced storage, along with dynamic optimization of grid operation and resources, digital information and control, distributed resources, demand-side resources, energy efficiency resources, and smart metering systems. The developments in intelligent controllers reflect in power flow management also. This paper gives an idea of how to achieve power flow management through a tariff-based Fuzzy Logic Controller (FLC). The control algorithm used here is the k factor-based Icosϕ algorithm, which decides the amount of power shared by microgrids and the grid based on the load demand. The power forecasting of the microgrid with a solar source is performed using ANN. The regression plot and the error histogram obtained show the accuracy of the ANN controller. The simulation of power flow management of the smart microgrid system is tested and analyzed and the proposed system is compared with other existing research. The hardware implementation of the FLC controller with the Arduino interface which is analogous to real-time measurement is explained in detail to compare the simulation results obtained. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction/purpose: Among excellent strategies available to control the torque of asynchronous motors, we distinguish direct torque control. This technique of control allows direct control of magnetic flux and electromagnetic torque without the need to decouple them. Also, direct torque control like each control strategy has some drawbacks, the major drawbacks of this technique being operation at a variable switching frequency and flux and electromagnetic ripples due to the use of hysteresis regulators. It worsens acoustic noise, especially at low speeds, as well as the control performances. Methods: To improve the performance of direct torque control especially at low speeds, the authors propose using fractional order PID in combination with type-2 fuzzy logic controllers to regulate the speed of an induction motor controlled by direct torque control. Results: The results obtained by the proposed regulators show the improvements made to the system. Conclusion: The proposed contribution can exert better control efforts.

The exponential increase in the dependence on electrical power by rapid industrialization and increasing population has given rise to the need of supplying electric power efficiently and with good power quality (PQ). The evolution of renewable energy sources and their increased penetration into traditional grid systems has increased the complexity of electric power transmission and distribution. The significant increase in the use of complex converters, Flexible AC Transmission System devices and complex nonlinear loads in modern power systems have amplified the issues associated with the production and distribution of electricity having proper PQ. The proper functioning of the power system network requires healthy quality of power, maintained both at the load and source end. The Dynamic Voltage Restorer is a prospective D-FACTS (Distribution Flexible AC Transmission System) device that has been widely incorporated for addressing the PQ issues caused by irregularities in the distribution grid's voltage, current, or frequency. This research article attempts to enhance PQ indices of Photovoltaic, Fuel Cell, DVR and energy storage (battery) based Microgrid systems through a proposed Scaled Conjugate based Artificial Neural Network (SC-ANN) in the Matlab/Simulink architecture. The suggested technique’s effectiveness is verified by introducing severe PQ faults such as sag and swell and numerous power system responses have been comprehensively studied by comparing them with traditional Fuzzy Logic Controller and Proportional Integral controllers. The results obtained validates the efficiency of the proposed controller over FLC and PI methods in maintaining the system power indices constant during the onset of PQ faults by reducing harmonics and oscillations thereby enhancing the overall system reliability, efficiency and stability paving its way for real-time implementation in the areas of MG system.

This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module’s operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios.

Requirement of energy for automobiles is increasing everyday as we take a step towards development. The use of nonrenewable energy sources such as fossil fuels is leading to an increase in pollution. There was research performed by the United States Environmental Protection Agency (EPA) in 2007 and it was found that a normal family car produces approximately three to four tonnes of carbon dioxide per year, similarly six tonnes of carbon dioxide was produced by a large four-wheel drive like a van yearly. The present energy scenario of energy generation and the pollution caused by automobiles due to conventional energy sources have made it necessary to harness energy from other unorthodox sources to run them. Electric vehicles have been developed to solve this problem, in a sense that they don’t release any harmful gases such as carbon dioxide and carbon monoxide. One major problem which still exists even in electric automobiles is that the electricity that they utilize is still generated from conventional sources. Thus, if we can somehow generate electricity from unconventional sources, it can solve our former problem. Wind energy fuel which can be harnessed from a moving vehicle is one of such unorthodox sources that could be harnessed and subsequently used to power automobiles. This paper is a review of different ways of how wind power can be harnessed to fully power an automobile. [ABSTRACT FROM AUTHOR]

Introduction/purpose: Among excellent strategies available to control the torque of asynchronous motors, we distinguish direct torque control. This technique of control allows direct control of magnetic flux and electromagnetic torque without the need to decouple them. Also, direct torque control like each control strategy has some drawbacks, the major drawbacks of this technique being operation at a variable switching frequency and flux and electromagnetic ripples due to the use of hysteresis regulators. It worsens acoustic noise, especially at low speeds, as well as the control performances. Methods: To improve the performance of direct torque control especially at low speeds, the authors propose using fractional order PID in combination with type-2 fuzzy logic controllers to regulate the speed of an induction motor controlled by direct torque control. Results: The results obtained by the proposed regulators show the improvements made to the system. Conclusion: The proposed contribution can exert better control efforts. [ABSTRACT FROM AUTHOR]

The exponential increase in the dependence on electrical power by rapid industrialization and increasing population has given rise to the need of supplying electric power efficiently and with good power quality (PQ). The evolution of renewable energy sources and their increased penetration into traditional grid systems has increased the complexity of electric power transmission and distribution. The significant increase in the use of complex converters, Flexible AC Transmission System devices and complex nonlinear loads in modern power systems have amplified the issues associated with the production and distribution of electricity having proper PQ. The proper functioning of the power system network requires healthy quality of power, maintained both at the load and source end. The Dynamic Voltage Restorer is a prospective D-FACTS (Distribution Flexible AC Transmission System) device that has been widely incorporated for addressing the PQ issues caused by irregularities in the distribution grid's voltage, current, or frequency. This research article attempts to enhance PQ indices of Photovoltaic, Fuel Cell, DVR and energy storage (battery) based Microgrid systems through a proposed Scaled Conjugate based Artificial Neural Network (SC-ANN) in the Matlab/Simulink architecture. The suggested technique's effectiveness is verified by introducing severe PQ faults such as sag and swell and numerous power system responses have been comprehensively studied by comparing them with traditional Fuzzy Logic Controller and Proportional Integral controllers. The results obtained validates the efficiency of the proposed controller over FLC and PI methods in maintaining the system power indices constant during the onset of PQ faults by reducing harmonics and oscillations thereby enhancing the overall system reliability, efficiency and stability paving its way for real-time implementation in the areas of MG system. [ABSTRACT FROM AUTHOR]

Copyright of Tikrit Journal of Engineering Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study explores the guidance of a robotic arm along a predefined path by implementing an optimal fuzzy fractional order PID controller-based control strategy. This method serves as a means to address the nonlinearity and unpredictability of the robotic manipulator, contingent upon the fuzzy logic controller's specifications and the employment of a clonal selection algorithm. The dynamic equation of the manipulator was considered as an initial point, followed by designing a fuzzy controller for this purpose. To validate the effectiveness of this approach, it was compared to other techniques, such as Fuzzy, Fuzzy-PID, and fuzzy-FOPID controllers, with PID and FOPID controller parameters optimized using clonal selection algorithms. Simulation results reveal that the fuzzy-FOPID variant outperformed other methods under varying load conditions and model uncertainties, using SIMULINK/MATLAB 2014a.

Numerous research laboratories engaged in developing advanced algorithms for controlling Renewable Energy Systems (RES) face constraints due to the absence of specialized equipment for conducting research and experiments. Real-time simulation platforms are well-suited for creating fast or real-time computation models, offering prompt, accurate, and reliable insights for RES control implementation. Nevertheless, the high cost of real-time platforms renders them unaffordable for many laboratories. To address this issue, a cost-effective platform is designed to assist researchers in their activities. The hardware solution involves utilizing a Programmable Logic Control (PLC) to acquire data from diverse sensors and regulate the system through actuators and PI controllers. From a software perspective, a communication framework between MATLAB and the PLC is developed using OPC-UA (Open Platform Communications Unified Architecture) technology. In this paper, the operation and efficiency of the platform have been tested using the Software in the Loop (SIL) technique. The operating model is designed using MATLAB/Simulink to simulate and analyze the appropriate control technique suitable for PLC implementation, whereas the control is executed through Siemens S7–1200 PLC using TIA PORTAL. A visualization interface is established using WinCC-RT. Moreover, this paper presents the platform's design, discusses its application, and illustrates its usage through a case study. Indeed, a complex algorithm developed in MATLAB/Simulink can be automatically executed by any PLC using the designed framework.

Nowadays, multilevel inverters (MLIs) are widely used for integration with renewable energy (RE) resources and in industrial applications. Among different RE sources, photovoltaic (PV) energy is generally used for the generation of power. For accumulating more power from PV systems, the Fuzzy Logic Controller (FLC) technique is used to trace the maximum power (MPP). The FLC computes the reference PV voltage guarantee to ensure optimal power transmission between the PV system and the MLI's connected load. In this paper, a novel 5L (five-level) PV-based switched-capacitor MLI topology (PV-SC-MLI) has been projected with the target of reducing the number of switches and boosting the input voltage without any bulky transformer. A selected harmonic elimination pulse width modulation technique (SHEPWM) based technique is used to remove the harmonic for 5L-PV-SC-MLI. The projected technique is appropriate for all levels of MLIs, including non-equal and equal DC sources. The SHEPWM technique is used to solve the non-linear transcendental equations and obtain the optimal values of switching angles. The obtained switching value is used to reduce the total harmonic distortion (THD) of 5L-PV-SC-MLI. The THD at the output voltage of proposed inverter has been obtained value is 8.72% without filter and with filter 4.20%, which lies within the limits of IEEE 519 standards.

This research article presents a modified novel crisscross augmented ladder (CCAL) structured multilevel inverter (MLI). MLIs can be operated in symmetric and asymmetric binary and ternary voltage ratios. The modified structure comprises a generalized unit (CCAL) and an extendable structure; this structure can be extended to generate more stair case waveform. The foremost benefit of this modified structure is to curb the conduction path of active switches. The utilized structure uses only four active conduction paths in all modes. However, an MLI has complexity, such as a higher number of switches and bulky controlling driver circuits which need superior controls. This article suggests a prominent solution for the above issues. The subtle CCAL is a governed multicarrier pulse width modulation scheme with the savvy fuzzy logic controller and, therefore, added benefits, such as lower switch stress, lower switching loss, and lower dv/dt stress. Hitherto, many topologies are emerging to curb the component count reduction structure; among them it is an attempt to curtail the active conduction path. The working capability of the presented system is substantiated with a simulation study carried out in MATLAB R2017a and viability hardware (Xilinx FPGA) proof of concept to validate the effectiveness. [ABSTRACT FROM AUTHOR]

The main goal of this work is of developing a control approach, which is able to obtain the smooth switching between energy sources, battery, and Supercapacitor (SCAP). With four separate math functions, a new math function-based (MFB) controller is designed, and this MFB will generate four output signals corresponding to the motor's speed. Further, the MFB is combined with an FLC/PI controller to reach the theme of the work. Two-hybrid different controllers are intended as per the proposed control strategy termed as MFB with FLC and MFB with PI and both are implemented individually in MATLAB/Simulink in four different modes. The entire model is implemented including a solar panel to charge the battery, this solar panel (SP) is connected to the battery and UDC through various control switches. Finally, a comparative analysis is made between two hybrid controllers to know the better-performed controller. [ABSTRACT FROM AUTHOR]

Wireless sensor nodes remain vulnerable to malicious attacks in hostile environments. Adversarial attacks capturing a few benevolent nodes and creating multiple duplicate identities make the localization process very tricky. Incorrect position estimations often lead to the wrong decision regarding the monitored event. This paper presents a secure localization scheme mitigating such attacks using fuzzy-based anchor mobility control. All nodes periodically broadcast signals over the network using omnidirectional antennas. The anchor each time forms an individual cluster using some neighboring nodes with an Received Signal Strength (RSS ) exceeding a prescribed threshold. The mobility control algorithm keeps the anchor moving along a path that allows cluster formations at two successive anchor positions containing at least the nodes encountered first in the previous case. The current cluster size and the node location with minimal signal strength are two determining factors for the fuzzy controller to explore a new anchor path. The misbehaving nodes are detected and blocked by observing anomalies in node identities within the subsequent two clusters. The anchor contains an adaptive antenna and remains location-aware with a Global Positioning System (GPS) receiver. The anchor determines the Angle of Arrival (AoA ) information of all the signals intercepted in each cluster using such an antenna. Then, the anchor generates and transmits beacon messages to each legitimate node. Upon receipt of two beacon messages, the nodes localize themselves autonomously and switch to power-saving mode. The simulation results corroborate its competence comparable to existing methods for localization accuracy and intrusion detection precision. [ABSTRACT FROM AUTHOR]

Low density parity check (LDPC) codes are highly regarded for their exceptional error correction capabilities, making them a preferred choice for error correction coding (ECC) in modern communication standards. These codes are implemented in specialized integrated circuits (ICs) that facilitate data transmission. However, the power requirements of complementary metal-oxide-semiconductor (CMOS) circuits, which operate across a wide frequency range, present a significant challenge for future communication systems. To address this challenge, this paper proposes a novel multi-frequency power reduction strategy for LDPC encoders. The strategy utilizes dynamic voltage and frequency scaling (DVFS), a well-established power reduction method in CMOS circuits. By combining DVFS with fuzzy logic control, the system optimizes the voltage supplied to the LDPC encoder, achieving substantial power reduction while maintaining coding efficiency, flexibility, and performance. The proposed approach dynamically adjusts the encoder's voltage levels based on real-time conditions, ensuring efficient power consumption across different frequencies. This innovative strategy aims to overcome the power challenges faced by future communication generations, which have been largely overlooked in previous research efforts. This paper aims to assess the effectiveness of a multi-frequency power reduction strategy for LDPC encoders. Through extensive analysis and evaluations, it demonstrates significant power reduction while maintaining LDPC coding efficiency. These findings contribute to the development of power-efficient LDPC encoder designs, meeting the evolving needs of communication systems. Results reveal the system's performance across different frequency ranges. In the lowfrequency range (1 MHz to 100 MHz), a remarkable 90% power reduction is achieved. In the medium-frequency range (100 MHz to 1 GHz), the reduction is 50%, while in the high-frequency range (1 GHz to 5 GHz), it reaches 20% to 14.5%. Notably, the system operates most effectively in the low and medium frequency range, providing substantial power savings. These results highlight the potential of the multi-frequency power reduction strategy for LDPC encoders in addressing power challenges across different frequencies. The significant power reduction achieved demonstrates its effectiveness and adaptability to varying frequency demands. This contributes to the development of power-efficient LDPC encoder designs, optimizing performance and energy consumption in various communication systems. [ABSTRACT FROM AUTHOR]