Intelligent Fuzzy Logic Controller Research Articles

Detection of Sybil & DDoS attacks in VANET using intelligent technique

ABSTRACT This paper proposes a novel fuzzy logic controller (FLC) based model referred to as FBM to detect both the DDoS and the Sybil attacks in VANET using an intelligent fuzzy logic controller. A VANET environment is created with different numbers of attackers to check the efficacy of the proposed model using veins, which is used for simulation with an event-based network simulator OMNeT++ and a road traffic simulator SUMO. Furthermore, the performance of the proposed model is compared with a few other machine learning techniques, such as logistic regression (LR), the decision tree (DT), random forest classifier (RF), and the support vector machine (SVM). The proposed FBM model yields better performance than the existing machine-learning techniques. The proposed FBM performs better than RF in accuracy by 0.10492%, precision by 0.08065%, recall by 0.02505%, and F1 score by 0.088243% for the DDoS scenario. It is also observed that the FBM performs better than DT in accuracy by 3.91486%, precision by 14.39994%, and F1 score by 3.819618% for the Sybil scenario. The margin of error for the proposed FBM is also estimated for 95% of the confidence interval and is found to be 0.021691 for the DDoS scenario.

A new power management strategy for PV-FC-based autonomous DC microgrid

Solar energy is widely available in nature and electricity can be easily extracted using solar PV cells. A fuel cell being reliable and environment friendly becomes a good choice for the backup so as to compensate for continuously varying solar irradiation. This paper presents simple control schemes for power management of the DC microgrid consisting of PV modules and fuel cell as energy sources and a hydrogen electrolyzer system for storing the excess power generated. The supercapacitor bank is used as a short term energy storage device for providing the energy buffer whenever sudden fluctuations occur in the input power and the load demand. A new power control strategy is developed for a hydrogen storage system. The performance of the system is assessed with and without the supercapacitor bank and the results are compared. A comparative study of the voltage regulation of the microgrid is presented with the controller of the supercapacitor bank, realized using a traditional PI controller and an intelligent fuzzy logic controller.

FUZZY FRACTIONAL ORDER SLIDING MODE CONTROL FOR OPTIMAL ENERGY CONSUMPTION-TRANSIENT RESPONSE TRADE-OFF IN ROBOTIC SYSTEMS

This work presents a new fuzzy logic parameter tuning-based fractional order sliding mode control (F-FOSMC) strategy for the trajectory tracking of robotic systems. The primary objective is to achieve an optimal control compromise seeking to minimize energy consumption for required motions, all while increasing both accuracy and rapidity of the transient response, addressing therefore a critical challenge in the field of robotics. Our approach integrates a sophisticated fuzzy logic fractional order mechanism to dynamically fine-tune the noninteger derivation parameter of the FOSMC, offering adaptability to varying operational conditions. This intelligent fuzzy logic controller tuning optimizes the FOSMC performance, demonstrates a remarkable capacity to handle uncertainties and disturbances intrinsic to robotic applications and allows to optimize the trade-off between the energy consumption and transient response efficiency throughout the robot’s motion. The effectiveness of the proposed study is thoroughly examined via simulations conducted on a 3 DOF manipulator robotic system. Results showcase that F-FOSMC significantly reduces control energy consumption while preserving rapid and efficient transient response characteristics.

A Centralized Stage-Wise Control Approach for Frequency and Voltage Regulation in PV–Wind–Storage Based Islanded Microgrid

In a microgrid, perturbations stemming from uncertain renewable sources and sudden load fluctuations can result in deviations in system voltage, frequency, and more, leading to instability. Thus, focusing on achieving stable microgrid operation, this paper proposes a novel stage-wise control approach for regulating the frequency and voltage of islanded microgrids. The approach employs an intelligent fuzzy logic controller with 64 rules. Specifically, the control approach is divided into three stages. In stage 1, the proposed approach manages the system under minor perturbations to regulate all system parameters within a feasible operating range, utilizing a battery as ancillary support. Furthermore, during significant perturbations, stages 2 and 3 of the proposed approach involve percentage-wise shedding non-critical loads to enhance stable microgrid operation. The load-shedding process continues until the system parameters stabilize within an acceptable operating range. The performance of the proposed approach is validated using MATLAB software and is compared against existing topologies. Notably, it has been observed that in the presence of significant small and large perturbations, the system demonstrates considerably lower deviations, following the recommended standards, i.e., 0.57 and 0.72% voltage deviation, including 0.14 and 0.17% frequency deviation. This observation emphasizes the superiority of the proposed control approach.

Optimal design, intelligent fuzzy logic and model predictive control for high-purity ethyl-methyl carbonate and diethyl carbonate production using reactive dividing wall column

Methyl carbonate (EMC) and Diethyl carbonate (DEC), as excellent solvents for lithium-ion electrolytes, are synthesized by transesterification, and consecutive reaction seriously complicate optimization and control issues. Process synthesis, parameters optimization and advanced control are crucial points that should be addressed for this ill-conditioned and highly nonlinear system, which is the motivation and novelty of present work. With single-objective and multi-objective genetic algorithm, intensified processes for EMC and DEC production were optimized respectively with heterogeneous catalyst. And reactive dividing wall column achieved 23% and 13% total annualized cost reductions compared with reactive distillation for two target carbonates. Based on optimal steady-state solution, intelligent fuzzy logic and model predictive control of EMC and DEC production is conducted to clarify their respective strengths in intensified process with consecutive reaction. Sequential optimization for parameters of FLC was carried out with Integral of Squared Error (ISE) as objective. Finally, control performance of FLC and LPMC are compared with that under PID control, and results show that overshoot and transition time under FLC and LMPC are obviously mitigated, along with evident reduction in ISE.

An Experimental Testing of Optimized Fuzzy Logic‐Based MPPT for a Standalone PV System Using Genetic Algorithms

The choice and the dimensioning of the controller for the maximum power point tracking (MPPT) are determined for the ideal energy efficiency of the photovoltaic (PV) systems. Many works have been developed in the field of MPPT methods, especially fuzzy logic controllers. However, these are robust if the parameters of the membership functions have been well designed. In this paper, the performances of an intelligent fuzzy logic controller (FLC)‐based MPPT method have been optimized by an evolutionary genetic algorithm (GA). The works presented in the literature have shown the efficiency of the proposed method compared to classical methods. In our paper, the validation of the experimental results obtained is given with respect to a reference signal. The control of the simulated PV source and the proposed method are built using the Simulink/Matlab environment and implemented on the dSPACE DS1104 controller to validate the practical execution of the suggested method. The standalone PV system has been tested in an emulated test bench experimentation. Experimental results confirm the efficiency of the proposed method and its high accuracy in handling fast varying load conditions.

Experimental validation and intelligent control of a stand-alone solar energy conversion system using dSPACE platform

This paper presents the performances of an artificial intelligent fuzzy logic controller (FLC) based maximum power point tracking (MPPT) and a conventional perturb and observe (P&O) based MPPT controller is presented for a stand-alone PV system and tested in an emulated test bench experimentation. The studied system is composed of a DC power supply emulating the PV panel, a DC/DC boost converter, a variable resistive load and a real-time MPPT controller implemented in the dSPACE DS1104 controller. To verify the performance of the FLC proposed, several simulations have been performed in Matlab/Simulink environment. The proposed method outperforms the P&O method in terms of global search capability and dynamic performance, according to the comparison with the P&O method. To verify the practical implementation of the proposed method, the control of the emulated PV source and the MPPT algorithms are designed using the simulink/Matlab environment and implemented on dSPACE DS1104 controller. Experimental results confirm the efficiency of the proposed method and its high accuracy to handle the resistance varying.

Fuzzy Logic-Based Load Frequency Control in an Island Hybrid Power System Model Using Artificial Bee Colony Optimization

This study presents the implementation of Artificial Bee Colony (ABC) optimization in an island hybrid power system model using fuzzy logic-based load frequency control. The Island Hybrid Power System considered in this study consisted of various generation units and an energy storage system. The optimized control parameters of PID using ABC were used in an intelligent fuzzy logic controller. The profiles (power & Frequency) of isolated hybrid power system were improved using a Super Conducting Magnetic Energy Storage (SMES) System. Individual controllers were used for wind turbine and diesel generators to control the power output for balancing the demand (frequency change control). Comparative analysis of power and frequency with the help of various classical and intelligent control configurations is presented. The outcome of the study shows that a minimum deviation in frequency and power is obtained through the proposed Intelligent Fuzzy Control approach for the considered isolated power system model.

Rule-Based Fuzzy Controller for Solid State Transfer Switch Towards Fast Sensitive Loads Transfer

This article deals with the development of an intelligent fuzzy logic controller for solid-state transfer switch toward fast fault detection and power transfer to the alternative feeder. A set of fuzzy rules are developed for the controller which in turn shift the loads from the preferred feeder to the alternate feeder with quality power resolving fault interruption or voltage sags. The voltage and the current control loop generate a signal to the fuzzy control block to provide an on or off pulse for the switches upon activate fuzzy rules in the interference engine. In this article, a compilation of 35%, 50%, 70%, and 100% faults are created to demonstrate the effectiveness of the proposed fuzzy controller toward detecting fault levels and transferring power to the desired feeder. It was discovered that the proposed fuzzy logic controller detects faults in the range of 0.054 to 0.061 milliseconds and transfers power in the range of 1.742 to 3.107 ms, which is significantly faster and more reliable than the existing literature.

A grid-connected DVSI with features to enhance power quality

A dual-source voltage inverter system is proposed to improve the power and consistency aspects of using a microgrid. Evaluation of the Proportional Integral (PI), the intelligent Fuzzy Logic Controller (FLC), and the developed control method is known as the Instantaneous Symmetrical Component Theory (ISCT). A Dual Voltage Source Inverter (DVSI) uses Distributed Energy Resources (DER) to exchange power and compensate for unbalanced and non-linear loads in the system. The direct-quadrature-zero transformation (Dq0) conversion is used to obtain the positive sequence voltage. To evaluate the system control method, an inverter connected to a three-phase, four-wire distribution combination is used. The proposed system is verified by MATLAB simulation methods using a PI controller and an intelligent FLC system.

Hybrid Renewable Energy Source Combined Dynamic Voltage Restorer for Power Quality Improvement

In this paper, the hybrid photovoltaic-thermoelectric generator (PV-TEG) combined dynamic voltage restorer (DVR) system is proposed for the power quality disturbances compensation in a single-phase distribution system. The stable and precise level of input voltage is essential for the smooth and trouble-free operation of the electrically sensitive loads which are connected at the utility side to avoid system malfunctions. In this context, the hybrid PV-TEG energy module combined DVR system is proposed in this paper. With the support of the hybrid energy module, the DVR will perform the power quality disturbances compensation effectively with needed voltage and /or power. In the proposed system, the PV and TEG energy sources are connected electrically in series to produce adequate voltage for the DVR operation and the fractional factor-based variable incremental conduction (FFVINC) maximum power point tracking (MPPT) control algorithm is employed to extract the possible maximum power from the PV array. The intelligent fuzzy logic controller (FLC) is chosen for implementing the MPPT control algorithm. The half-bridge voltage source inverter (VSI) circuit and in-phase voltage compensation technique are used in the DVR for better power quality disturbances compensation. The performance and usefulness of the proposed DVR system are investigated by an extensive simulation study with four different modes of operation, the study results are confirmed that the proposed system promptly identifies the power quality disturbances for compensation. Moreover, the investigation proved that the combined PV and TEG energy module can provide better energy efficiency in converting solar irradiation into electricity.

A fuzzy rule based approach for islanding detection in grid connected inverter systems

<span lang="EN-US">Islanding is when an area of the electrical distribution system is isolated from the electrical system while being powered by distributed generators. An important condition for the interconnection of power plants and distribution systems is the ability of the power plant to detect islands. The presented and proposed method is a combination of best active sandia frequency shift (SFS) method with the intelligent fuzzy logic controller, which has been tested in distributed production using the island detection function. And the choice to improve the method by fuzzy logic control (FLC) is retained, as this process is more effective in decreasing the non-</span><span lang="EN-US">detection zone (NDZ) and in further improving the efficiency of the islanding detection system. This paper proposes a new active islanding detection technique controlled by a fuzzy logic controller, for grid connected photovoltaic (PV) inverters. In addition, the efficiency and performance of the proposed method strategy for islanding detection has been analyzed and tested in the various situations of the network. In addition, the results of the simulations with the <span lang="EN-US">power </span><span lang="EN-US">simulation (</span>PSIM) software will be provided to illustrate the main conclusions and the development of the control. Thus, will be used to show the feasibility and validity of the proposed new algorithm.</span>

Indirect Effective Controlled Split Source Inverter-Based Parallel Active Power Filter for Enhancing Power Quality

The existing solutions for reducing total harmonic distortion (THD) using different control algorithms in shunt active power filters (SAPFs) are complex. This work proposes a split source inverter (SSI)-based SAPF for improving the power quality in a nonlinear load system. The advantage of the SSI topology is that it is of a single stage boost inverter with an inductor and capacitor where the conventional two stages with an intermediate DC-DC conversion stage is discarded. This research proposes inventive control schemes for SAPF having two control loops; the outer control loop regulates the DC link voltage whereas the inner current loop shapes the source current profile. The control mechanism implemented here is an effective, less complex, indirect scheme compared to the existing time domain control algorithms. Here, an intelligent fuzzy logic control regulates the DC link voltage which facilitates reference current generation for the current control scheme. The simulation of the said system was carried out in a MATLAB/Simulink environment. The simulations were carried out for different load conditions (RL and RC) using a fuzzy logic controller (FLC) and PI controllers in the outer loop (voltage control) and hysteresis current controller (HCC) and sinusoidal pulse width modulation (SPWM) in the inner loop (current control). The simulation results were extracted for dynamic load conditions and the results demonstrated that the THD can be reduced to 0.76% using a combination of SPWM and FLC. Therefore, the proposed system proved to be effective and viable for reducing THD. This system would be highly applicable for renewable energy power generation such as Photovoltaic (PV) and Fuel cell (FC).

INVESTIGATING THE POTENTIAL CONSEQUENCES OF THE MEMBERSHIP FUNCTIONS IN A FUZZY LOGIC CONTROLLER-BASED OBSTACLE CLIMBING ROBOT

Autonomous mobile robots are the robots which can perform desired tasks in unstructured environment without continuous human guidance. This paper deals with the design of an Autonomous obstacle climbing robot that will able to climb an obstacle. This obstacle climbing navigation system is utilized by the intelligent fuzzy logic controller. The controller has 2 inputs and single output system. The inputs are slope and terrain type and the output is the speed of the robot. The membership functions are the building blocks of fuzzy logic system and play a vital role in the performance of mobile robot. This paper describes the effect of different membership functions namely triangular, trapezoidal membership functions are considered.

Optimization of Perturb and Observe Based Fuzzy Logic MPPT Controller for Independent PV Solar System

In this paper, a novel intelligent Fuzzy Logic Controller (FLC) based on Maximum Power Extracting (MPE) of photovoltaic standalone system is proposed, which can handle the uncertainties of the rules under high deviations in climate conditions. Traditional Perturbation and Observation (P&O), hillclimbing MPE structures are examined. The MPE used a boost converter controller. The novel MPPT controller develops enhanced P&O based on possible power extractor search technique by fuzzifying rules of such method and eliminates the conventional drawback. A new FLC based improved P&O offers a precise and quick convergence to MPE during changing climatic and steady-state conditions compared to existing P&O and hill climbing method. The development of the suggested MPE is confirmed in simulation MATLAB at dissimilar operating circumstances.

ASYMMETRICAL FUZZY LOGIC CONTROL BASED MPPT ALGORITHM FOR STAND-ALONE PV SYSTEM UNDER PARTIALLY SHADED CONDITIONS

Partial shading conditions (PSCs) in Photovoltaic (PV) system is an inevasible situation which curtails the PV array output by exhibiting multiple peaks in its Power-Voltage (P-V) curve. The multiple peaks consist of a single global maximum power point (GMPP) and many local maximum power points (LMPP). The presence of multiple peaks makes tracking of maximum power point more difficult and demands an efficient controller to track the global peak of the P-V curve. In the present work, a novel intelligent asymmetrical Fuzzy Logic Control (AFLC) based maximum power point tracking (MPPT) algorithm has been proposed for tracking GMPP. The fuzzy membership functions of the proposed algorithm have been optimized using a heuristic approach. The algorithm has been designed, developed and analyzed using MATLAB/Simulink. Furthermore, to establish the superiority of proposed AFLC algorithm, it has been compared with conventional perturb & observe (P&O) algorithm and intelligent Fuzzy Logic Control (FLC) based algorithm for GMPP tracking and shading losses under standard test condition (STC) and partially shaded conditions.

Modelling and simulation of fuzzy-based MPPT control of grid connected PV system under variable load and irradiance

The photovoltaic (PV)-based distribution generation system has a nonlinear power characteristic curve under random variation in solar irradiance, ambient temperature and electric load. As a result, for the accurate detection and tracking of the maximum power points (MPPs), it is necessary to design an optimal controller with dynamic control capability. As solution to the above issue, this paper presents an intelligent Mamdani-based fuzzy logic controller (M-FLC) for maximum power point tracking (MPPT) of a PV system. Different test cases with respect to different possible load and irradiance variations in grid connected mode of operation are investigated. To confirm the power quality indices within IEEE standards specification, fast Fourier transform (FFT) analysis of voltage and current at the point of common coupling has been done. A detailed comparison has been made in between PV without MPPT, with incremental conductance and proposed fuzzy logic control (FLC). The results show an enhance efficiency of energy production from PV and reflects the effectiveness of the proposed scheme justifying its real time application.

An Intelligent Controller of Nonlinear Conical Tank Water Level System

The present research presents an intelligent fuzzy logic controller (FLC) system for control water level of nonlinear systems, whereas the cross-section area of the vertical water is not constant (conical tank). The mathematical model of the conical tank level system was derived and its simulation runs were carried out by considering the FLC. For comparative analysis, a similar test runs were also carried out by means of conventional ZN based PI-mode. Interestingly, the results illustrate that applying the FLC system in the control loop in the conical tank system could provide a good tracking performance than that of conventional PI model.

Artificial Intelligent Fuzzy Logic Controller Applied on 6DOF Robot Arm Using LabVIEW and FPGA

In this work an efficient Artificial Intelligent Robotic Fuzzy Logic Controller (AIRFC) system have been constructed to control the robot arm. A serial link Robot manipulator with 6 Degree of Freedom (DOF) from DFROBOT of code ROB0036 is used as a case study. A fuzzy logic type1 controller is implemented on LabVIEW to control each joint of the robot arm for nonlinearity measurements and a fuzzy logic type2 controller is applied which is more suitable for uncertainty. The hardware design is implemented and finally downloaded using the Field Programmable Gate Array (FPGA) kit named PCI-7833R from National Instrument. By using the LabVIEW FPGA MODEL the target board can be detected for software implementation of the controllers’ systems. The work shows that in case of type2 fuzzy logic the rise time is less than that of type1 fuzzy logic for the shoulder, wrist roll and the gripper angles and it is higher for base, elbow and wrist pitch angles. The settling time is the same in elbow and wrist pitch angles and for the type2 fuzzy controller it is less for other angles.

Artificial Intelligent Fuzzy Logic Controller Applied on 6DOF Robot Arm Using LabVIEW and FPGA

In this work an efficient Artificial Intelligent Robotic Fuzzy Logic Controller (AIRFC) system have been constructed to control the robot arm. A serial link Robot manipulator with 6 Degree of Freedom (DOF) from DFROBOT of code ROB0036 is used as a case study. A fuzzy logic type1 controller is implemented on LabVIEW to control each joint of the robot arm for nonlinearity measurements and a fuzzy logic type2 controller is applied which is more suitable for uncertainty. The hardware design is implemented and finally downloaded using the Field Programmable Gate Array (FPGA) kit named PCI-7833R from National Instrument. By using the LabVIEW FPGA MODEL the target board can be detected for software implementation of the controllers’ systems. The work shows that in case of type2 fuzzy logic the rise time is less than that of type1 fuzzy logic for the shoulder, wrist roll and the gripper angles and it is higher for base, elbow and wrist pitch angles. The settling time is the same in elbow and wrist pitch angles and for the type2 fuzzy controller it is less for other angles.