Fuzzy Logic Controller Research Articles

Type-2 of Fuzzy Logic Control Optimization in Interleaved Bidirectional Converters for Battery Performance

Type-2 of Fuzzy Logic Control Optimization in Interleaved Bidirectional Converters for Battery Performance

Fuzzy Logic Controller-Based STATCOM for Reactive Power Compensation in a Grid-Connected Wind Power Plant

Wind energy is one of the most prevalent renewable sources nowadays, which has been projected to play an immense role in realizing the clean energy transition. This paper dives into one of the critical challenges in the grid integration of wind power plants, which is reactive power compensation. The static synchronous compensator (STATCOM) is commonly used to inject/absorb reactive power in wind power plants where the device is controlled with the traditional proportional-integral (PI) controller. This article investigates the effectiveness of the fuzzy logic controller (FLC) in managing the STATCOM to compensate and regulate the reactive power in a 9 MW grid-connected wind power plant. Due to its faster and malleable response, the FLC allows for greater flexibility in controlling the STATCOM. According to the findings, the voltage profile at the point of common coupling (PCC) is improved considerably in the case of the FLC compared to the PI controller, demonstrating the efficacy of the employed strategy. MATLAB-Simulink has been used to model and analyze the proposed system. We achieved a 6 percent improvement in voltage regulation when FLC was utilized instead of PI controllers. DUJASE Vol. 8 (1) 62-70, 2023 (January)

Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

AbstractThe increasing global need for renewable energy sources, driven by environmental concerns and the limited availability of traditional energy, highlights the significance of solar energy. However, weather fluctuations challenge the efficiency of solar systems, making maximum power point tracking (MPPT) systems crucial for optimal energy harvesting. This study compares ten MPPT approaches, including both conventional and artificial intelligence (AI)‐based techniques. These controllers were designed and implemented using MATLAB Simulink, and their performance was evaluated under real environmental conditions with fluctuating irradiance and temperature. The results demonstrate that conventional techniques, such as incremental conductance (INC), Perturb and Observe (P&O), Incremental conductance and Particle Swam Optimization (INC‐PSO), Fuzzy Logic Control and Particle Swam Optimization (FLC‐PSO), and Perturb and Observe and Particle Swam Optimization (P&O‐PSO), achieved accuracies of 94%, 97.6%, 98.9%, 98.7%, and 99.3% respectively. In contrast, AI‐based intelligent techniques, including Artificial Neural Network (ANN), Artificial Neural Fuzzy Interference System (ANFIS), Fuzzy Logic Control (FLC), Particle Swam Optimization (PSO), and Artificial Neural Network and Particle Swam Optimization (ANN‐PSO), outperform achieving higher accuracies of 97.8%, 99.9%, 98.9%, 99.2%, and 99%, respectively. Compared to available research, which often reports lower accuracies for conventional techniques, our study highlights the enhanced performance of AI‐based methods. This study provides a comprehensive comparative analysis, delivering critical analysis and practical guidance for engineers and researchers in selecting the most effective MPPT controller optimized to specific environmental conditions. By improving the efficiency and reliability of solar power systems, our research supports the advancement of sustainable energy solutions.

Closed-Loop Multimodal Neuromodulation of Vagus Nerve for Control of Heart Rate.

The use of electrical current to modulate neurons for autonomic regulation requires the ability to both up-regulate and down-regulate the nervous system. An implanted system employing this electrical neuromodulation would also need to adapt to changes in autonomic state in real-time. Stimulation of autonomic nerves at frequencies in the range 1-30 Hz has been a well-established technique for increasing neural activity. Vagus nerve stimulation (VNS) has been shown to be sensitive to frequency adjustments, which can be used to more precisely control the effect as compared to amplitude modulation. Kilohertz frequency alternating current (KHFAC) is a proven technique for blocking action potential conduction to reduce neural activity. Additionally, KHFAC can be reliably modulated by simple amplitude modulation. Although there are many types of commonly used closed-loop controllers, many conventional methods do not respond well to long system delays or discontinuities. Fuzzy logic control (FLC) is a state-based controller that can describe the discontinuities of the system linguistically and then translate the state transition to a continuous output signal. In our preparation, a single bipolar electrode was placed on the vagus nerve and controlled by a fuzzy logic controller to deliver both stimulation and KHFAC to control heart rate. The FLC was able to both change the heart rate to selected values and maintain the heart rate at a constant value in response to a physiological perturbation.

Optimal placement of light sensor for improving energy efficiency and visual comfort in smart buildings

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.

Optimal fuzzy P + Dµ controller for cancer chemotherapy

This paper aims to develop a robust, effective, and optimal controller for an automatic intravenous drug delivery system for the chemotherapy treatment of cancer patients. For this purpose, a novel hybrid control method, namely the Fuzzy P + Dµ controller, is designed by combining fractional calculus and fuzzy logic controller (FLC). Particle swarm optimization (PSO) algorithm is employed to tune the controller parameters optimally. To evaluate the performance of the proposed controller, a cancer patient model that considers cumulative drug toxicity, one of the most common side effects of chemotherapy, is utilized. Limits on toxicity and drug concentration for patient safety are incorporated into the controller design and optimization process. The graphical and numerical simulation results show that almost no cancerous cells are left at the end of the treatment period. The effectiveness of the controller is proven by the performance index (PI) obtained. Our optimal hybrid Fuzzy P + Dµ outclasses other control techniques in previous related studies with the best PI of 44.426 and the minimum final number of cancerous cells (FNCCs) of 5.084E-08. Moreover, in robustness tests perturbing intra-patient parameters and using different treatment protocols, the cumulative drug toxicity level is kept within safe limits, and almost no cancerous cells remain.

Fuzzy Based Quick Acting DC Link Voltage Controller for Three Phases of D-Statcom Connected to the Microgrid

The microgrid systems are the combinations of solar photovoltaic, wind energy, fuel cell and battery systems, as well as conventional generators for backup. The micro grid system barriers that are encountered for their integration to the network. In power systems, voltage stability and power quality are major problems. Due to fluctuations in dc link voltage, changes in load may have an effect on the compensation. This work offers a fuzzy logic-based supervisory approach to enhance the performance of the dc link transient. The controllers employ proportional and integral gain changes with fuzzy logic PI controllers integrated with distribution static synchronous compensator (D-STATCOM). These changes occur in the transient response phase right after a load fluctuation. In order to verify the suggested controller, mathematical equations are given to compute the gains of the traditional controller using fuzzy-logic PI fast-acting dc-link voltage controllers for obtaining similar quick transient reaction. Fuzzy logic PI controller enhancements that employ greater sampling during the transient period and expert knowledge of system behavior enhance the controller's performance. Compared to a standard PI controller, a dc link error reduces the capacitor voltage during a load shift. Utilizing the suggested method and the outcomes of MATLAB simulations, it will be demonstrated that efficiency and the voltage's quick settling time.

Fuzzy Logic and Neural network-based self-tuned PID controller of Quadcopter

Quadcopters have been established as a viable tool for the current era of technology. Quadcopters are also known as drones or unmanned aerial vehicles. The quadcopter can be used for aerial surveys for deliveries, filming, and missions such as detecting and mapping unreachable areas, hills, forests metropolitan areas, rocks, and obstructions that can be a danger or risk to navigation for commercial and security projects. It conducts its survey mission without human operator intervention. After the mission is accomplished, data can be fetched by GPS or the Quadcopter returns to a pre-programmed location where the data can be downloaded and processed. It can lead to an improvement in the position tracking performance due to an intrinsic change in the object's weight, buoyancy, and aerodynamic forces. In addition, quadcopters must navigate the extremely dynamic airborne environment that is represented by urban, rural, metropolitan, or natural environments. In light of this, efficient dock tracking is required to attain the requisite high precision performance, resulting in less time consumption, when the system's dynamic characteristics are time-dependent or the operating conditions and vision guidance alter. The automatic identification of objects by classifying images and self-tuning the gains of a PID (Proportional + Integral + Derivative) controller has been proposed in this paper. The object recognition is based on machine learning guided vision and self-tuning PID controller based on fuzzy logic and neural network.

Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle

In order to attain the optimal allocation of energy between auxiliary power unit (APU) and battery of the connected automated range-extended electric vehicle, from a multi-scale perspective, an Approximate Optimal Energy Management Strategy (AOEMS) has been proposed. Firstly, a composite determination method was designed based on prediction and parameter identification to divide the operating condition types, which could be as an operating condition prediction and feed forward control method to determine the approximate optimal power distribution between APU and battery. This method keeps APU operating at the optimal operating point/area as much as possible without predicting the accurate vehicle speed sequence. Then, an adaptive method based on V2X information was used to adjust the key threshold parameters in real-time using a fuzzy logic controller which reduces the algorithm complexity with prediction and division of the operating conditions types, which has significantly improved the robustness and overall performance of the optimization. Finally, the simulation and experimental results thoroughly indicated that the proposed AOEMS can better balance the performances, as anticipated, enhancing economy, reducing emissions, and extending battery lifewere effectively maintained in equilibrium.

A modular multiport Landsman converter-driven hybrid EV charging station with adaptive power management system

This article presents the implementation of a hybrid charging station that utilizes renewable energy sources to power its operations. The core of this system is a modular multiport Landsman converter, which serves as the interface between diverse renewable sources, including photovoltaic panels, wind turbines and fuel cells. A sophisticated multi-agent-based energy management system is employed to address the intermittency of renewable energy supply throughout the day. Additionally, a fuzzy logic controller (FLC) facilitates the seamless charging of electric vehicles within the system. The FLC detects the state of charge (SoC) of the vehicle's battery and the battery present in the station, enabling efficient EV charging. The multi-agent energy management system with load shedding controller (MAEMLS) and FLC continuously monitor the power availability, power demand, charging time, and charging duration of the charging station. This approach reduces the charging rate for EV owners during peak demand periods. The proposed system is modelled and tested using MATLAB/SIMULINK software, and a laboratory prototype has been constructed. The results demonstrate that the anticipated converter is highly suitable for integrating hybrid energy sources and charging electric vehicles (EVs). Notably, this converter exhibits an efficiency ranging from 85 %-95.05 %, based on the availability of input DC sources.

Fuzzy Neural Network Control for a Reaction Force Compensation Linear Motor Motion Stage

Technological progress and market expansion in the semiconductor industry require ultra-precise and high-speed processing and motion systems. However, even though reaction force compensation (RFC) linear motor motion stages help reduce the transmitted reaction force, the residual vibration of the movable magnet track after motion acts as a disturbance, affecting settling time and tracking performance. To address this issue, this study introduces a fuzzy neural network controller for an RFC linear motor motion stage. Since non-linear factors, such as frictional forces from guide rails, make it difficult to accurately model the system, a Nonlinear Autoregressive Neural Network (NARX) is used to identify the nonlinearity from displacement and servo control outputs. The fuzzy logic controller, which takes into account the error and error rate, is then combined with the inverse model controller of NARX. Finally, the fuzzy neural network controllers are integrated with the existing PIV (proportional-integral-velocity) control in feedforward configurations. The experiments demonstrate the potential performance improvements of the proposed approach over the PIV controllers.

Hardware implementation of type-2 fuzzy logic control for single axis solar tracker

Solar tracker widely maximizes solar energy harvesting by maintaining a perpendicular relative position between the sun and the solar panel. Single and dual-axis solar tracker controllers are the most control mechanisms that are widely implemented. The single-axis solar tracker (SAST) is preferable between those two control mechanisms due to economic and simpler control algorithm features. Many control algorithms have been proposed to improve the performance of SAST. The conventional proportional integral derivative (PID) controller has major limitations mainly corresponding to slower response. Moreover, it cannot handle the uncertainties of the sunlight. To overcome the problem, type 2-fuzzy logic control (T2-FLC) is proposed. The single-axis solar tracker controller based on T2-FLC is applied in Arduino and implemented in the hardware environment. It was monitored that the T2-FLC provides much better responses than the conventional controllers in terms of better dynamic response and more efficiency in harvesting solar energy.

A comprehensive review on fuzzy logic control systems for all, hybrid, and fuel cell electric vehicles

A comprehensive review on fuzzy logic control systems for all, hybrid, and fuel cell electric vehicles

Comparing Temperature and Humidity Control Using PID and Fuzzy Logic in a Climatic Chamber

The presence of a thermohygrometer is important in some places, especially in hospitals and climate room equipment. A climate chamber is an enclosed space or isolated environment, which will provide the environmental conditions of relative humidity and temperature. In accordance with the Decree of the Minister of Health of the Republic of Indonesia. Certain rooms such as rooms in hospitals require special attention to environmental conditions, such as the surgical process that occurs in the operating room. A thermohygrometer is a tool used to monitor room conditions. The thermoygrometer used must be able to trace the measurement results using certain media. A climate chamber is a device that provides the desired climate regardless of the external environment. The purpose of this study was to analyze the comparison of PID and Fuzzy Logic temperature control systems on the Climatic Chamber (Fuzzy Logic System) device which plays a role in the process of measuring room temperature and humidity in the field. The method used in this study was to compare directly with a previously calibrated thermohygrometer. The measurement results at 25°C have a response time of 7 minutes 30 seconds and an overshoot of 0.1°C, at a temperature of 30°C has a response time of 5 minutes 15 seconds and an overshoot of 0.1°C, at a temperature of 35°C has a response time of 5 minutes 30 seconds and 0.2°C overshoot. At 50%RH Humidity has a response time of 13 minutes 30 seconds, at 60%RH Humidity has a 12 minute response time, At 70%RH Humidity has a 6 minute response time. The measurement results show that fuzzy logic control has more advantages than PID control. Fuzzy logic control has a faster response time to setpoint than PID control and fuzzy logic control has smaller overshoot compared to PID control.

Analysis of Load Frequency Control of Two Area Power System using Different Control Techniques

To maintain stability is a mandatory requirement of any kind of power network. Interconnected power systems have significant advantages in maintaining stability and frequency at their nominal values. The control of the interconnected power systems is performed by Automatic Generation Control (AGC), which has two parts: the Automatic Voltage Regulator (AVR) and Load Frequency Control (LFC). When a disturbance occurs in the system, Load Frequency Control (LFC) acts to re-establish the system into its steady state with zero error as well as the desired transient response characteristics. This paper exhibits the design and implementation of a PID and Fuzzy Logic Controller for two area power systems, taking the steady state error and percentage of overshoot, undershoot, and pre-shoot as comparison parameters. MATLAB/Simulink software was used to bring out the implementation and obtain results. System dynamic performance is observed for PID and Fuzzy Logic Controllers in Simulink. The comparison study indicated that the proposed Fuzzy Logic controller has better performance than the PID controller. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 62-79

Comparison of Fuzzy Logic Controllers for Speed Regulation in Separately Excited DC Motors: Field Versus Armature Control

Comparison of Fuzzy Logic Controllers for Speed Regulation in Separately Excited DC Motors: Field Versus Armature Control

Fuzzy Logic Control of Two-Stage Grid- Connected Photovoltaic System

In this paper, a two-stage grid-connected photovoltaic system is presented. The optimal performance of solar panels at varying temperatures and irradiance levels is achieved by the application of a fuzzy logic-based maximum power point tracking method. Space vector modulation is used to control the inverter; to achieve capacitor voltage balance, redundant switching states of a three level inverter are used. The grid side control regulates the power and the current injected to the grid, the conventional PI controllers are replaced by fuzzy PI. Simulations have been made using Simulink environment on MATLAB to validatethe control of the proposed system.

Advanced Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI

This paper proposes a methodology for the design of electronic control unit (ECU) hardware units with increased performance and reliability. Today's vehicles are equipped with dozens of ECUs that significantly influence the system's efficiency, reliability, performance, and safety. With the increased complexity of control algorithms and the environmental constraints that automotive systems operate, the robustness and efficiency of the ECUs are of utmost importance. In this work, an approach is proposed based on combining hardware redundancy, commercial field programmable gate arrays (FPGAs), and artificial intelligence strategies to provide increased redundancy checks and robust control. An additional redundancy is added to the hardware architecture of the ECU to include a parallel hardware unit. The two controlling units operate in parallel. The output of each of them is compared, allowing redundancy checks in the computation of the output variable (oV) of the system. The mathematical model of the ECU depicts the governing equations of the ECU in the form of differential equations, which results in a corresponding state-space configuration. These mathematical models are encoded into the field programmable gate array (FPGA) and processed in hardware, leading to an equivalent software-based implementation. To analyze the performance of both models within the ECU, an artificial neural network (ANN)-based strategy is proposed. The ANN depicts the governing equations of the ECU in the form of differential equations encoded in the form of sigmoidal functions. To analyze the reliability of the control action in the ECU, the temperature of the system is increased, which leads to a random variation of the system parameters. The variability of the ECU parameters leads to a deviation in the computation of the oV and the corresponding control action. The robustness of the control is determined in such conditions. A control law is determined to guarantee proper control action under variations in the governing equations of the system. This control law is represented by a simple algebraic equation, which can then be cast in various control strategies, such as look-up tables or fuzzy logic controllers. Several cases are simulated to assess the performance of the proposed control law for the hardware redundancy scheme, for the ANN-based equivalent software implementation approach, and for the additional fuzzy logic controller. The simulation results are analyzed concerning the requested oV and give insight into the performance and reliability of the proposed dedicated ECU design.

Enhanced Control of Polymer Electrolyte Membrane Fuel Cell Fed Basic Series Positive Super Voltage Lift Luo Converter Using Interval Type-2 Fuzzy Logic

An eco-friendly and renewable energy source, Polymer Electrolyte Membrane (PEM) fuel cells offer various advantages such as stability and efficiency. The present study aims to control output voltage of PEM fed basic series positive super voltage lift luo (PSVLL) converter. The model, which consists of a fuel cell and a basic series PSVLL converter, was designed in Matlab/Simulink simulation environment. The control of the PSVLL converter output voltage was performed using an interval type-2 takagi-sugeno-kang fuzzy logic (IT2TSKFL) and type-1 takagi-sugeno-kang fuzzy logic (T1TSKFL) controllers comparatively. The simulation study results demonstrated that the PEM fuel cell fed basic series PSVLL converter reached optimal current and voltage values with IT2TSKFL under nominal and different operating conditions. Moreover, IT2TSKFL improved average settling time of control system by 22.335% compared to T1TSKFL controller.

HYBRID ANTI ISLANDING PROTECTION SCHEME FOR DISTRIBUTED GENERATORS BASED ON FUZZY LOGIC CONTROLLER

Distributed generators are blessing for power grid. Distributed generators should operate properly during and after islanding process. Here in this work the main focus is earlier islanding detection and to detect islanding on earlier. Hybrid islanding protection can be preferred for islanding detection. An active oscillatory disturbance is introduced in controller of the converter and fuzzy logic controller is used, so that the introduced signal gets amplified during islanding and it is helpful to detect the islanding. Generally proportional gain controller is used for islanding detection, but its response is slow, hence fuzzy logic controller is used. Fuzzy Logic controller gives earlier trip signal to system than proportional integral controller and small signal stability issues get improved. The system performance is discussed for zero power mismatch conditions. The simulation results of this proposed method is evaluated by using MATLAB Software.