Fuzzy Logic Control Algorithm Research Articles

تصميم متحكم المنطق الضبابي لتتبع أقصى نقطة قدرة لنظام الطاقة الكهروضوئية

This research paper presents the method of fuzzy logic system to optimize the energy extraction in a photovoltaic (PV) power system. The maximum power of a (PV) module varies due to changing temperature, solar radiation, and load. To maximize efficiency, PV systems use a maximum power point tracker (MPPT) to constantly extract the highest power that can be produced by a solar panel and then deliver it to the load. The general structure of an (MPPT) system contains a (DC-DC) converter (an electronic device that converts a source of direct current DC from one voltage level to another) and a controller. The (MPPT) finds and maintains operations at the maximum power point using a tracking algorithm during variations in weather conditions. Because of the nonlinear behavior of (PV) module current-voltage characteristics and the nonlinearity of (DC-DC) converters due to switching, conventional controllers are unable to provide the best response, especially when dealing with wide parameter variations and line transients. The goal of this work is to design andimplement a maximum power point tracker that uses a fuzzy logic control algorithm. Fuzzy logic naturally provides a superior controller for this type of nonlinear application. This method also benefits from the artificial intelligence approach for overcoming the complexity in modeling nonlinear systems. In order to succeed in this work, an (MPPT) system consisting of a (PV) module, a (DC-DC) converter, and a fuzzy logic controller is designed and simulated in Simulink. Analyses of buck- boost converter characteristics are carried out to find the most suitable topology for the PV system used. An integrated model of the PV module with the identified converter is simulated in MATLAB to derive the expert knowledge needed to formulate and tune the fuzzy logic controller. The simulation results show that, the fuzzy logic controller is able to obtain the desired outcomes which are actual power generation of solar panels at different irradiance and temperature. Keywords: Fuzzy logic, PV, MPPT, DC-DC converter, MATLAB programming

Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.

Application of Artificial Intelligent Techniques for Power Quality Improvement in Microgrid System

This paper focuses on modeling, control and power quality improvement of a microgrid connected system. This last was designed as a multi-converter system with Wind Turbine driven Permanent Magnet Synchronous Generator, and lithium ion Battery Storage Energy System. These sources are connected by a continuous bus to a nonlinear load through a DC /AC converter and three-phases multi-functional voltage source inverter. Where, wind systems are considered as primary power resource, and the grid is used for the effect of consumption of the overage power available from sources, when the battery has been fully charged. Multi Functional Voltage Source Inverter is used to ameliorate the performance of the proposed system, guaranteeing both reactive power and harmonic compensation. Moreover an intelligent control by fuzzy logic control algorithm are managed In order to extract the maximum power from wind turbine, to guarantee an effective storage management, this about the DC side. For the AC side a direct power control strategy with fuzzy logic algorithm have been used. The simulation of the system solution is carried out in Matlab/Simulink. The obtained simulation results show that the technique of fuzzy logic furnish the best solution in term of robustness, optimization performance, low THD and fast dynamic response.

Hierarchical equalization scheme for retired lithium-ion battery packs based on inductor-flyback transformer

Some lithium-ion batteries can be reused after sorting and recombination. Equalization of these batteries during the process of step utilization is an effective way to improve the inconsistency of battery packs and extend their lifespan. This paper proposes a hierarchical equalization topology based on a combination of inductor and transformer, which is divided into intra-group and inter-group battery equalization. The intra-group equalization circuit achieves energy transfer between non-adjacent individual batteries through an improved Buck-Boost circuit. The inter-group equilibrium circuit exchanges energy between any battery subpackages and the entire battery pack using a flyback transformer. An LCD (Inductance-Capacitor-Diode) lossless absorption network is added to the transformer to absorb voltage spikes caused by leakage inductance while achieving soft switch to reduce switch losses. Considering that retired lithium-ion batteries can affect battery capacity and State of Charge (SOC) estimation accuracy due to aging issues, a modified Extended Kalman Filter (EKF) algorithm is proposed to improve battery SOC estimation accuracy. A fuzzy logic controller is introduced into the SOC-based control strategy to improve the equalization efficiency of the battery pack. Finally, to validate the effectiveness of the proposed equalization scheme, a model is built and simulated using MATLAB/Simulink software. The experimental results show that compared with the traditional double-layer inductor equalization scheme using the most value equalization algorithm and fuzzy logic control algorithm respectively, this equalization scheme has a faster equalization speed and higher equalization efficiency.

Increasing the energy efficiency of an electric vehicle powered by hydrogen fuel cells

The article considers fuel cell electric vehicle energy efficiency improvement by means of traction battery remanent state of charge modern control algorithms application which based on fuzzy logic. Fuel cell vehicle traction system used as experimental setup for the study includes brushless dc motor, lithium-ion batteries pack and electronic differential approach implementation. The results of simulation modelling in MATLAB-Simulink software comparing conventional PID-controller based and fuzzy logic based controller for vehicle speed control systems are presented. Fuzzy logic controller provides an electric vehicle dynamic behavior tuning for different driving modes automatically transferring the control system to economy mode when it possible. However, the control system is able to provide fast dynamic behavior when it is required. The proposed control algorithm is tested by means of developed experimental setup. Experimental results validate conducted simulation modelling results and the proposed control algorithm. Application of the proposed fuzzy logic control algorithm to control the electric vehicle dynamic behavior allows energy efficiency improvement and driving range increase.

Fuzzy logic-based coordinated operation strategy for an off-grid photovoltaic hydrogen production system with battery/supercapacitor hybrid energy storage

The coupling of photovoltaic power generation with water electrolyzer is advantageous for enhancing solar energy utilization and generating green hydrogen. In this work, an off-grid photovoltaic-based hydrogen production system consisting of photovoltaic, electrolyzer, battery energy storage system and supercapacitor was developed. A coordinated operation strategy is designed to manage the power of each unit in the system to avoid significant fluctuations in working power and frequent start-stop operations of the electrolyzer unit. Firstly, the coordinated operation strategy sets five operation modes for the photovoltaic-based hydrogen production system, and the fuzzy logic control algorithm is used to choose the operation mode to determine the reference power of each unit. On this basis, the state of charge feedback-based first-order low-pass filter algorithm is used to achieve the power allocation to mitigate deep charging and discharging of the supercapacitor. The proposed solution is extensively assessed through comparative simulation experiments based on three typical test days. The numerical results confirm its effectiveness and demonstrate the potential for achieving high system efficiency.

Fuzzy logic-based control for robot-guided strawberry harvesting: visual servoing and image segmentation approach

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

Vibration control of structure using active tuned mass damper: A new control algorithm

This paper investigates the vibration behavior of a 10-story benchmark building controlled with an active tuned mass damper (ATMD) under earthquake loads. An ATMD, typically installed on the top floor, is a modified form of a tuned mass damper (TMD) that includes a mass, spring, damper, and an actuator enhancing system performance and structural damping. The force and movement direction of the ATMD are controlled by a processor and actuator. In the ATMD system, control algorithms determine the magnitude and direction of the applied force. This research introduces a new control algorithm based on the structure’s dynamic response and ATMD stiffness. To evaluate the performance of this algorithm, linear–quadratic regulator (LQR) and fuzzy logic controller (FLC) algorithms were designed and presented for comparison. The results indicate that the proposed algorithm outperformed the other algorithms, reducing the structure’s displacement and acceleration responses by an average of 40% and 28.16%, respectively, compared to the uncontrolled state.

Control of mobil robots in fuzzy environment

In this work, the application of fuzzy hardware control to the dynamic movement of an autonomous two-wheeled mobile robot under uncertainty is considered the most relevant topic of the day. There are also adverse reactions. These led to an imprecise mathematical model for the two-wheeled robotic test rig. Therefore, the goal of developing a control system for a two-wheeled robot with a fuzzy logic control algorithm was set. The development of fuzzy logic control systems according to modern applications is very important to create intelligent robots that can be used in wider applications. In this work, a fuzzy control model for a non-linear two-wheeled robot system is designed, followed by a real-time implementation of the developed control in a robot device. Finally, the experimental results of the system were analyzed and discussed, and the closed-loop response of the fuzzy logic two-wheeled robot system with PID control was tested. The fuzzy control has a better response than the PID controller. A similar effect was observed for the normalized speed of the motors, as less energy was required to balance and stabilize the two-wheeled robot with fuzzy control. One of the main advantages of the method proposed in the article is to enable the autonomous two-wheeled mobile robot to operate without collision with obstacles and with high dynamic accuracy in a low-obstacle environment. As a result of the simulation, the effectiveness and reliability of the change avoidance behavior in uncertain environments and the speed control of the two-wheeled mobile robot platform of the proposed fuzzy control strategy are realized.

Implementation of fuzzy logic control algorithm for temperature control in robusta rotary dryer coffee bean dryer

Background: Indonesia is one of the coffee producers ranked third in the world in the supply of coffee beans. To maintain competitiveness international market, it is necessary to maintain and improve the quality of coffee beans.Objective: One crucial aspect of maintaining the quality of coffee beans is maintaining the moisture content of green coffee beans. One of the water content settings is using the drying method. While traditional drying methods often experience weather and long-time constraints.Results: This study designed an innovative coffee bean dryer based on fuzzy logic to overcome the problem. This system uses temperature control with Mamdani's fuzzy logic control interference algorithm, input and delta errors, and output percentage valve opening. This method achieved a moisture content following SNI standards of 12% and a 0.00015% / s drying rate for each coffee bean mass increased by 1kg. This method is also more efficient and stable in maintaining the temperature at a value of 50°C.Methods: The drying equipment also estimates the drying time by considering variations in the mass of coffee beans. This dryer can provide an effective solution to maintain optimal coffee bean quality.Conclusion: The second semi-wash method of drying coffee beans using a fuzzy logic-based coffee bean drier has proven successful for drying coffee beans to a moisture content of 12% in a period of 90 min to 195.65 min with a drying capacity of 1 kilogram to 10kg at 50°C.•The coffee beans utilized in the studies are robusta coffee beans from plantations on Mount Kawi's slopes in East Java, Indonesia.•The trial sample was 1 kilogram of green coffee beans removed from the horn skin.•According to SNI standards, the drying performed is the second in the postharvest semi-wash procedure to achieve a moisture content of 12%.

Optimization of hybrid platform for high-tech equipment and building vibration mitigation using evolutionary algorithms

In this paper, an ideal fuzzy logic control (FLC) algorithm is designed for micro vibration control of a hybrid platform which is placed on a second floor of the building subjected to traffic-induced ground vibrations. A hybrid platform is one that can be utilized to safeguard high precision equipment from vibrations caused by traffic, machinery, and natural hazards like earthquakes while it is situated with in the building structure. A fuzzy system is employed to obtain the appropriate control force of active actuators, and the fuzzy system is then optimized using four evolutionary algorithms (EAs), which are population-based metaheuristic algorithms that were inspired by the nature. A microgenetic algorithm (µ-GA), particle swarm optimization (PSO), differential evolutionary algorithm (DEA), and cuckoo search algorithm (CSA) are used to optimize the FLC knowledge base and rule base. To illustrate the capabilities of the proposed method in traffic-induced vibration control of a hybrid platform, a three-degree-of freedom (3-DOF) structure along with the platform is taken into account. Finally, utilizing the Bolt, Beranek and Newman (BBN)-vibration criteria and the absolute velocity of hybrid platform as the objective, the efficiency of the hybrid platform microvibration control is evaluated. The simulation results obtained from these novel EAs indicated that the actively controlled platform is efficacious in reducing microvibration of high-tech equipment. Based on the results, the PSO algorithm optimized fuzzy controller outperforms the other methods in terms of velocity levels of second floor of the building and hybrid platform.

Fuzzy logic controller algorithm for placing files in a data storage system

Fuzzy logic controller algorithm for placing files in a data storage system

Implementation of Fuzzy Logic Controller Algorithms with MF optimization on FPGA

In this work, we propose the design and implementation of a parallel-structured fuzzy logic controller with integral action and anti-windup. The Grey Wolf Optimization (GWO) optimization technique is used to optimize fuzzy rules, which allows for the complicated algebraic ideas of type 1 fuzzy logic algorithms to be reduced to straightforward numerical equations for FPGA target implementation. The techniques for operating a geared DC motor are optimized by the membership function structure of our controller's data propagation. Our proposed controller was implemented in Xilinx System Generator (XSG) and co-simulated on hardware and software with VIVADO and XSG tools.

Design of an Improved Modular Multilevel Converter Reconfigurable Equalization Scheme Based on Difference of Voltage Variation

Abstract In order to solve the problem of lower available capacity and shorter cycle life due to the barrel effect of series-connected batteries, as well as the problem of pseudo-equalization caused by battery aging, this paper proposes a modified modular multilevel converter (MMC) reconfigurable equalization scheme with difference of voltage variation (DOVV) as the equalization variable. The equalization topology consists of an MMC circuit and a voltage regulator, which effectively solves the problems of low total available capacity, inefficient energy transfer, and slow equalization by reducing the number of switches and achieving independent control of the equalization and voltage regulator modules. A control strategy based on the Oxford aging dataset is proposed with DOVV as the equalization variable, and a fuzzy logic control algorithm is introduced according to the distribution characteristics of DOVV data. This equalization control strategy overcomes the pseudo-equalization phenomenon due to battery aging. The simulation results show that compared with the traditional DC–DC energy transfer equalization topology, the energy transfer efficiency of the proposed equalization topology is improved by 62.15% and the equalization time is reduced by about 16.36%, and the pseudo-equalization phenomenon caused by the aging of the battery pack during the equalization process is well overcome. The feasibility of the proposed equalization scheme is verified.

Research on equalization scheme of lithium-ion battery packs based on consistency control strategy

In order to address the inconsistency problem of series-connected lithium-ion battery groups in practice, a two-level balanced topology based on bidirectional Sepic-Zeta circuit is designed in this article. Two-level equalization topology uses bidirectional Sepic-Zeta circuits both within and between groups, which can achieve the equilibrium between any cells in a battery group and between any battery groups. With the state of charge (SOC) of the battery as the equalization variable, and the equalization control strategy is designed based on the consistency controller and PI controller to achieve fast and efficient equalization by dynamic adjustment of equalization current, and to efficiently decrease the inconsistency of the equalized battery packs. The experiments are conducted by Matlab/Simulink platform. According to the experimental results it can be obtained that compared with the topology based on single Cuk equalizer, the topology based on single Sepic-Zeta equalizer and the adjacent topology based on Sepic-Zeta equalizer, the proposed topology shortens the equilibrium time by about 51.57 %, 51.43 % and 42.24 %, respectively. Under the same operating conditions, the equalization control strategy designed in this article shortens the equilibrium time by about 29.4 % and 11.19 % compared to the conventional fixed-threshold method and the Fuzzy Logic Control (FLC) algorithm, respectively, and also achieves better results in the equalization efficiency and consistency problem, which effectively verifies the feasibility of the equalization scheme in this article.

Wavelet transform‐based real‐time energy management strategy of hybrid energy storage system for electric vehicle

SummaryThe peak and transient components of demand power caused by the complex and variable traffic environment could induce the accelerated degradation of the battery lifespan for electric vehicle (EV). This paper proposes a wavelet transform‐based real‐time energy management strategy (EMS) to fully exploit the advantages of the hybrid energy storage system (HESS). First, to adapt the characteristics of battery and ultracapacitor, wavelet transform is employed to decompose driving cycle into high frequency power and low frequency power. Second, since the wavelet transform is difficult to be directly implemented in real‐time control, a power prediction model including four neural network predictors is established, which are trained by the data obtained from wavelet transform to online predict the power of different frequencies. Third, the high frequency power and the low frequency power are distributed to the battery and ultracapacitor by fuzzy logic control (FLC) algorithm, which can significantly reduce the damage caused by current rapid changes into the battery. Accordingly, the battery lifespan is effectively extended because it significantly avoids the impact of rapidly changing and peak current. Finally, simulation results verify the effectiveness of wavelet transform‐based real‐time EMS, and the proposed EMS robustness against temperature is verified at different temperatures. Compared with the wavelet transform strategy (WTS), the peak currents of wavelet transform‐fuzzy logic control strategy (WTFLCS) are decreased by 26.07%, 25.66%, and 25.85% at 10°C, 25°C, and 40°C, respectively.

Stand-alone Micro Grid based on Artificially Intelligent Neural Network (AI-NN)

INTRODUCTION: Hybrid stand-alone Small Wind Solar Energy System offers a feasible solution in remote areas where grid connectivity is either financially or physically unavailable. A small wind turbine (SWT) and a solar photovoltaic system are part of the hybrid energy system, which is effectively employed to meet the energy needs of rural household loads. OBJECTIVE: This research suggests an effective analysis of wind solar hybrid system controllers taking energy demands into account. The controller should be designed in such a way as to intelligently monitor the availability of wind energy and solar energy and store the energy without spilling it out. METHODS: In order to cope with the challenging factors involved in designing the controller, intelligent power tracking with an artificially intelligent neural network (AI-NN) is designed. Added to that, the whole process has been designed and analysed with the MATLAB SIMULINK tool. RESUSTS: The results of the simulation, infer that AI-NN achieved the regression value of 0.99 when compared with the Perturb & Observe algorithm (P&O), and the Fuzzy Logic Control (FLC) algorithm, and has a higher tracking speed. Also, the AI-NN attained 2.62kW whereas the P&O has attained 2.52kW and Fuzzy logic has attained 2.43W of power which is 3.89% higher than P&O algorithm and 7.52% higher than fuzzy MPPT algorithm. CONCLUSION: The designed controller module enhances the system by artificially intelligent algorithm. The AI-NN attains the better power performance with lesser tracking time and higher efficiency. Thus, it is evident that AI-NN MPPT suits well for the hybrid system.

Fuzzy Logic Control of External Heating System for Electric Vehicle Batteries at Low Temperature

The reduction in driving range and the degradation of vehicle performance in cold weather has become one of the challenges in vehicle electrification in recent years. The root cause of this phenomenon is the property of lithium-ion batteries with capacity and power capability reduction at low temperatures. In this study, an external battery heating system was developed by employing an electrothermal film affixed to the surface of each cell, and the heating process was performed during driving. An equivalent circuit model combined with a thermal model was established for the simulation and control design. A fuzzy logic control strategy was developed to optimize the external heating power provided by the battery pack, and to achieve the maximum range by the end of discharge. A global optimal control strategy obtained by dynamic programming and a constant maximum power heating strategy were used for comparison. Simulation and experimental validations show that the proposed fuzzy logic control algorithm can achieve a 3.6% to 5.3% improvement in driving range than the maximum power heating method, and has close performance to the global optimal solution. Furthermore, the vehicle equipped with the proposed heating system can have up to 150.4% of the range recovery under different driving conditions.

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms.

Fuzzy Logic Control-Based Charge/Discharge Equalization Method for Lithium-Ion Batteries

Abstract In this article, a grouping equalization circuit based on the single-ended primary inductor converter (SEPIC) circuit is proposed, which can transfer energy between any single cell or grouped cells. Compared with the traditional equalization circuits that transfer energy between adjacent cells, the SEPIC circuit can directly connect any two batteries that need to be equalized; the number of circuit equalization paths is calculated based on a directed graph and then used as a basis for grouping the batteries to improve the equalization efficiency. In the charging or discharging condition, the amount of charge remaining in the battery to be charged or discharged is used as the control variable for equalization, and intragroup equalization is completed before intergroup equalization starts. To ensure the equalization efficiency of the battery, the equalization current is controlled by fuzzy logic control (FLC). Taking ten single cells as an example based on the calculation of the number of equalization paths, two 5-cell groups can be confirmed as the optimal solution. Experiments were performed on matlab/simulink simulation platform, and the results show that compared with the traditional adjacent inductance equalization circuit, the equalization circuit proposed earlier reduces the time needed for equalization by 35.8%; compared with the traditional average difference method, in charging and discharging conditions, the FLC algorithm saves times by 20.5% and 31.3%, respectively, and energy loss is reduced by 9.1% and 5.5%, respectively, which verifies the feasibility of the proposed equalization scheme.