Optimal Fuzzy Logic Controller Research Articles

Simulation of grid/standalone solar energy supplied reduced switch converter with optimal fuzzy logic controller using golden BallAlgorithm

This article presents the utilization of a shunt active power filter (SHAPF) in combination with an Energy Storage System (ESS) and a Solar Energy System (SES). Voltage source converters (VSC) are connected in parallel to a direct current (DC) bus. The membership function (MSF) of fuzzy logic controller (FLC) for the shunt control system is optimally adjusted using the golden balloptimization algorithm (GBOA). The present effort aims to achieve the following primary objectives: 1) Quick implementation to stabilize the voltage of the DC Link capacitor (DCLCV); 2) Mitigation of harmonics and improvement of power factor (PF); 3) Satisfactory performance under load as well as solar power varying conditions. The effectiveness of the optimally designed controller is evaluated by studying four test scenarios with grid and standalone conditions. The results are then compared to the existing sliding mode (SMC) and fuzzy logic controllers (FLC).

Design a robust and optimal fuzzy logic controller to stabilize the speed of an electric vehicle in the presence of uncertainties and external disturbances

In the nonlinear dynamic equations of the electric vehicle, parameters such as the coefficient of friction between the tires and the road, the coefficient of traction, the resistance of the anchor, and so on have ambiguities. Designing a controller that is robust to the existence of these parametric ambiguities and also to external disturbances, while still satisfying the optimality criteria, is a challenging task. In practical applications, in addition to the problems mentioned above, the computing load of the control input should also be taken into account and a sensible interaction between the performance desired by the controller and the computing volume should be offered. In the present work, a robust, optimally stable fuzzy controller based on parallel distributed compensation is designed using the Takagi–Sugeno fuzzy model of the electric vehicle. The fuzzy model stabilizer feedback gains, the upper bound of uncertainties, the upper bound of disturbance effect, and the upper bound of the cost function are obtained completely offline by solving a minimization problem based on the linear matrix inequality. Therefore, the calculation volume of the control input is extremely small. This allows the proposed control to be put into practice. The good performance of the proposed controller is demonstrated in five-stage simulations.

Multi-Converter UPQC Optimization for Power Quality Improvement Using Beetle Swarm-Based Butterfly Optimization Algorithm

The primary goal of this paper is to design a Multi-Converter Unified Power Quality Conditioner (MC-UPQC). The proposed method is solved based on the Synchronous Reference Frame (SRF) theory. The MC-UPQC involves two series Voltage Source Converters (VSCs) for power transfer between feeders to eliminate voltage sag, swell, interruption, and transient response in the system. The proposed model adopts control strategies based on an optimized Fuzzy Logic Controller (FLC) in SRF, utilizing a hybrid metaheuristic algorithm called Beetle Swarm-based Butterfly Optimization Algorithm (BS-BOA), which combines Beetle Swarm Optimization (BSO) and Butterfly Optimization Algorithm (BOA) for membership limit optimization and control rule generation. The major benefit of the optimized FLC-based MC-UPQC is its quick behavior in minimizing Total Harmonic Distortion (THD) in source and load side voltages and currents. Simulation and MATLAB environment are used for the entire system implementation. The comparative analysis of the proposed controller for MC-UPQC is performed against conventional controllers to validate its effectiveness. Quantitative data related to the main research outcomes, such as THD of source voltage and current, and dynamic behavior of the system, are included. The main benefit of the study is the significant reduction in THD and improved dynamic performance of the system.

Efficient MPPT Controller for Solar PV System Using GWO-CS Optimized Fuzzy Logic Control and Conventional Incremental Conductance Technique

Efficient MPPT Controller for Solar PV System Using GWO-CS Optimized Fuzzy Logic Control and Conventional Incremental Conductance Technique

Optimal fuzzy logic controller based PSO for photovoltaic system

In this study, an off-grid photovoltaic (PV) inverter generates three-phase power to supply the local load and is controlled using an optimized fuzzy logic controller (FLC) using particle swarm optimization (PSO) to control the photovoltaic system’s output. The PSO improves the three-phase inverter’s membership functions (MFs) by using the objective function as mean square error (MSE) for voltage at the output. The best-obtained outcome of the PSO is used to trigger the gate drives of the PWM inverter. The system has been simulated and modeled using MATLAB/Simulink to validate the performance of the proposed method and shows the best control output from the inverter and has been tested in a different types of loads. The motivation for using FLC to control the shape of MFs was about saving time consumed in trial and error. The results present an output of three-phase voltage and current waveform subjected to different loading conditions.

Chaotic-Billiards Optimization Algorithm-Based Optimal FLC Approach for Stability Enhancement of Grid-Tied Wind Power Plants

Globally,wind power plants (WPPs) installations are dramatically increasing, leading to a large-scale integration into the power grids. Huge endeavors are devoted to achieving an efficient operation of WPPs. This paper offers a novel chaotic-billiards optimizer (C-BO) algorithm to properly design the fuzzy logic control (FLC) strategy for stability enhancement of grid-integrated WPPs. The C-BO algorithm has distinct features such as simple construction, low numbers of parameters required to design, high convergence speed, and low computational burden. The permanent-magnet synchronous generator (PMSG)-based WPP is interlinked into the electric power grid through power converters. A cascaded FLC approach, which is optimally fine-tuned by the C-BO, is offered as the control methodology for these converters. The C-BO algorithm fine-tunes the gain factors of multiple FLCs at a rapid convergence speed. A simulation-based optimization approach is applied, in which the integrated square error criterion is chosen as an objective function. For the sake of preciseness, the PMSG-based WPP is connected to the IEEE 39-bus New England test system. The effectiveness of the optimal FLC using the C-BO algorithm is compared with that achieved using the water cycle algorithm-based optimal FLC, the genetic algorithm-based optimal FLC, and the marine predator algorithm-based optimal proportional-integral controller, taking into account severe grid disturbances. Moreover, real wind speed measured data captured from Zaafarana Station in Egypt are extensively performed in the system studies. The validity of the offered control approach is widely verified by the simulation analyses using the MATLAB/Simulink environment.

Fuzzy Logic Controller Optimized by BBO for Decentralized Source Based on a SOFC

This paper will investigate the efficiency of an optimal Fuzzy Logic Controller (FLC) for a decentralized source which is established on the basis of a Solid Oxide Fuel Cell (SOFC) that is linked to the electrical network through a voltage inverter and a boost converter. To serve the purpose of this research, a Biogeography Based Optimization (BBO) is applied in order to adjust the parameters of the membership functions (the centers and the widths of the gaussian membership functions in inputs and output) for the purpose of improving the efficacy of traditional fuzzy logic controller. The given control methods have proved to be effective drawing from simulation results, and show that fuzzy logic controller tuned by biogeography based optimization is better and more robust than the traditional fuzzy logic controller for decentralized source based on a SOFC.

Design optimization of fuzzy controllers in building structures using the crystal structure algorithm (CryStAl)

The current study uses a recently-developed metaheuristic method called Crystal Structure Algorithm (CryStAl) to achieve optimized vibration control in structural engineering. More specifically, this algorithm, which is inspired by the well-established crystallographic principles underlying the formation of crystalline solids in nature, is applied to the optimization of fuzzy logic controllers in building structures. To demonstrate the capability of this method in solving real engineering problems, two real-size building structures, one with three and the other with twenty stories, are considered. The fuzzy controllers are implemented through an active control system to control the seismically-induced vibrations of the structures intelligently. The evaluation criteria utilized to assess the overall performance of the optimization method applied to the fuzzy control system are presented and discussed. Through nonlinear structural analyses, the ductility, energy dissipation, and other nonlinear characteristics of the structures are also considered as the structural responses to be controlled. The computational results obtained from this novel metaheuristic algorithm are compared with those of the other expert systems from the optimization literature. The findings of this paper demonstrate that the Crystal Structure Algorithm is capable of outranking the other methods in the majority of considered cases.

Optimal fuzzy logic control of energy storage systems for [formula omitted] support in distribution networks considering battery degradation

High penetration of renewable energy resources into distribution networks induces frequency and voltage fluctuations to the power grids. Unlike high-voltage transmission lines, the x/r ratio of distribution lines is relatively low, thereby frequency support and voltage regulation are closely coupled. Considering their coupling relationship, a rule-based fuzzy logic controller (FLC) is proposed in this paper for battery energy storage systems (BESSs) to coordinately provide bus voltages and frequency support. The membership functions of the FLC are optimized offline to minimize the frequency and voltage deviations using Pareto front criterion. Moreover, with a practical battery aging dataset, a battery degradation model is constructed to design fuzzy rules for prolonging battery lifetime. The proposed method is tested on the IEEE 33-bus distribution network and compared with benchmarking methods. The simulation results verify that the proposed method can simultaneously provide frequency and voltages regulation support to satisfy the operational limits while extending the battery lifetime.

A Novel PSO Based Fuzzy Controller for Robust Operation of Solid-State Transfer Switch and Fast Load Transfer in Power Systems

This paper proposes a novel particle swarm optimization (PSO) algorithm based fuzzy logic controller (FLC) for improving the performance of automatic solid-state transfer switch (SSTS) with respect to transfer time. The proposed technique generates adaptive membership functions (MFs) of voltage error and rate of change of voltage error for input and output based on the fitness function formulated by the PSO. An optimal PSO-based FLC (PSOF) fitness function is further employed to tune and minimize the mean absolute error (MAE) to improve the performance of the load transfer in a short duration. Results obtained from the proposed PSOF are compared with those obtained with the conventional FLC to validate the developed controller. It is observed that the proposed PSOF optimized controller can transfer the load faster than the conventional FLC controller. The accuracy of the developed PSOF is illustrated and investigated via simulation tests for SSTS in the IEEE 9-bus system. It can be concluded that the PSOF controller is better than the only fuzzy controller in all tested cases in terms of transfer time.

Hyper-Spherical Search Optimized Fuzzy Logic Control Considering Operating Conditions for Hybrid Tram

Rule-based control strategy has significant advantages of strong robustness and high flexibility, and has gradually become a classical implementation method to optimize the energy management performance of modern energy-storage trams. However, it also faces the problems of over-reliance on expert experience and poor adaptability to operating conditions. Therefore, an improved dynamic power distribution method for tram lithium battery/supercapacitor energy storage system is proposed, which introduces road slope and running speed into the decision variable layer of traditional fuzzy logic control input. Firstly, the membership functions and domain are formulated according to operating conditions, the response time of high power density of supercapacitor is adjusted to improve the dynamic performance of the tram. Secondly, the hyper-spherical search algorithm is used to optimize the weight of fuzzy control rules, the peak current of the lithium battery is minimized and the life span of the energy storage system is increased. Finally, the fuzzy control strategies combined with different decision variables are compared and analyzed by using the data of modern tram western suburban line in Beijing. The results show that the proposed strategy achieves multiple superior to traditional fuzzy control strategy in power distribution, charge state offset ranges of lithium battery and supercapacitor, and energy storage system overall efficiency. Compared with the traditional fixed weight scheme, the peak current of the lithium battery is reduced by 31.02% and the continuous tram mileage is increased by 22.45% in the rule weight dynamic optimization scheme.

Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems

The performance of a photovoltaic (PV) array depends on temperature, radiation, shading and load size. Conventional maximum power point tracking (MPPT) methods have acceptable efficiencies under uniform conditions (irradiance = 1000 W/m² and temprature = 25 °C), but in dynamic weather conditions, load changes, and also in partial shading conditions due to the presence of several local maximum power points (MPP) in the P-V characteristic, the conventional tracking method does not work well in finding the main MPP. To extract maximum power in all condi-tions, many algorithms have been proposed, all of which have limitations in terms of convergence speed, output power ripple and efficiency. This research proposes an optimized Fuzzy Logic Controller (FLC) based on the Cuckoo Opti-mization Algorithm (COA) for MPPT under uniform conditions, dynamic weather conditions, partial shading and under load changes. Finally, the research compared the simulation results with four other popular methods. According to the simulation observations and the result, COA-FLC overcomes the mentioned limitations such as low convergence speed, output power ripple and low tracking efficiency in all conditions. Simulations are performed with MATLAB / Simulink software.

Robust fuzzy stability control optimization by multi-objective for modular vehicle

Modular design becomes a trend in the automotive industry to increase competitiveness with vehicle platforms that combine multiple modules to provide different applications. This paper presents an optimized Fuzzy Logic Control (FLC) applied to modular all-wheel-drive vehicles, focusing on Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEV) powered configurations. The vehicle behavior is determined by dynamic model simulation, in which the Magic Formula is applied to define the tire slip, associated with the load transfer during curves or drive/break situations. The controller acts as an electronic differential and changes the in-wheel motor torques to correct the vehicle trajectory during a standard maneuver. A multi-objective optimization based on the genetic algorithm determines the FLC configuration. The vehicle parameters (EV and HEV) have been modified to analyze the optimized FLC and, in all cases, the control showed an improvement in behavior to the vehicle without control. Finally, the FLC was implemented in a simple microcontroller and a hardware-in-the-loop simulation was developed to simulate a real vehicle operating condition and analyze this performance.

Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot

The energy-efficient motion control of a mobile robot fueled by batteries is an especially important and difficult problem, which needs to be continually addressed in order to prolong the robot’s independent operation time. Thus, in this article, a full optimization process for a fuzzy logic controller (FLC) is proposed. The optimization process employs a genetic algorithm (GA) to minimize the energy consumption of a differential drive wheeled mobile robot (DDWMR) and still ensure its other performances of the motion control. The earlier approaches mainly focused on energy reduction by planning the shortest path whereas this approach aims to optimize the controller for minimizing acceleration of the robot during point-to-point movement and thus minimize the energy consumption. The proposed optimized controller is based on fuzzy logic systems. At first, an FLC has been designed based on the experiment and as well as an experience to navigate the DDWMR to a known destination by following the given path. Next, a full optimization process by using the GA is operated to automatically generate the best parameters of all membership functions for the FLC. To evaluate its effectiveness, a set of other well-known controllers have been implemented in Google Colab® and Jupyter platforms in Python language to compare them with each other. The simulation results have shown that about 110% reduction of the energy consumption was achieved using the proposed method compared to the best of six alternative controllers. Also, this simulation program has been published as an open-source code for all readers who want to continue in the research.

Online Multi-Level Energy Management Strategy Based on Rule-Based and Optimization-Based Approaches for Fuel Cell Hybrid Electric Vehicles

This paper introduces an online multi-level energy management strategy (EMS) based on proposed rule-based and optimization-based approaches for fuel hybrid electric vehicles, including fuel cell, battery, and ultracapacitor systems. Our approach combines equivalent consumption minimization, state machine control, operational mode control, and fuzzy logic control methods. The proposed multi-level EMS reduces fuel consumption, enhances fuel cell operating time in a high-efficiency range, reduces battery power fluctuations, and improves maintaining the battery state of charge (SOC). The proposed EMS is compared with the optimized-fuzzy logic control (Opt-FLC) method. The results show a reduction in fuel consumption, battery power fluctuations, and the SOC difference between the start and end of the driving cycle, compared to Opt-FLC. Hence, fuel economy improvement and lifetime enhancement of hybrid energy storage system are the significant outcomes of new proposed multi-level EMS.

Real-Time Swing-Up Control of Non-Linear Inverted Pendulum Using Lyapunov Based Optimized Fuzzy Logic Control

This paper investigates the efficacy of an optimized fuzzy logic controller for real-time swing-up control and stabilization to a rigidly coupled twin-arm inverted pendulum system. The proposed fuzzy controller utilizes Lyapunov criteria for controller design to ensure system stability. The membership functions are further optimized based on the entropy function. The controller design is based on the black-box approach, eliminating the need for an accurate mathematical model of the system. The experimental results shows an improvement in the transient and steady-state response of the controlled system as compared to other state-of-the-art controllers. The proposed controller exhibits a small settling time of 4.0 s and reaches the stable swing-up position within 5 oscillations. Various error indices are evaluated that validates an overall improvement in the performance of the system.

Fuzzy Neural Optimized Fuzzy Logic Controller Based Dynamic Voltage Restorer for Power Quality Improvement with Non-linear Loads

Recently a large attention has been focused on a power quality domain due to: disturbances caused by non-linear loads, Increase in number of electronic devices and growth of renewable energy sources. Power quality measures the fitness of electric power transmitted from generation to industrial, domestic and commercial consumers. At least 50% of power quality problems are of voltage quality type. In power system voltage sags, voltage distortion introduced by harmonics, and asymmetrical voltage are considered to be the most severe affecting power quality, because both utilities and consumers are affected by these disturbances. Different methods introduced to solve power problems but the custom power devices are the most effective and efficient methods, one of which is the use of the Dynamic Voltage Restorer (DVR). The main objectives of this research are to achieve more accuracy in compensating the voltage variations and reducing the total harmonics distortion (THD) to acceptable limits. Most of the researchers applied the control strategies to compensate the voltage disturbances in critical load but did not focus on the objective of reducing the total harmonics distortion (THD). In many sensitive loads such as, airport lighting system, medical equipment, auxiliary plant of power system, and adjustable speed drives., the level of the (THD) is more important, this research focuses on mitigating the harmonics to less than 3%. An adaptive controller like fuzzy neural optimized fuzzy logic controller is proposed to improve the performance of the DVR in injecting the required voltage to restore the load voltage to its nominal value under different voltage variations which are created by MATLAB/SIMULINK program for a period of 0.15s from 0.8s and kept till 0.95s.

Optimal Fuzzy Logic Control for Temperature Control Based on Social Spider Optimization

Fuzzy Logic Controller (FLC) is useful for many applications like industrial, home, military and medical applications, so one of main application is temperature control of A proposed controller combines the advantages of controller structure and fuzzy inference for air heater temperature control applications. The input variables of the PDFLC-SSO are error and change of error, while its output is the control signal. PDFLC-SSO has seven membership functions. These functions are triangular shaped. Centroid method (CM) is selected for the defuzzification process. The universe of discourse range is used from (-1 to 1) for each input and output. Temperature heater model was utilized to test the controller; the gain factors was tuned using trial and error method and SSO algorithm to reach the optimum response with minimum settling time, minimum peak overshoot and minimum Integral Time Absolute Error (ITAE). Carried out simulations showed an average improvement in measurements using ITAE. ITAE is 7.46% for a constant set-value using PDFLC-SSO, while it is 9.55 for a constant set-value using PDFLC.

Optimization of fuzzy logic controller parameters using modern meta-heuristic algorithm for gantry crane system (GCS)

Robust control of underactuated nonlinear systems is a challenging task using conventional methods because of uncertainties which may lead to failure especially for mechanical and robotic applications. Fuzzy logic controller (FLC) is one of the methods specifically used for designing a robust controller that is able to deal with nonlinearity and achieve satisfactory performance for control applications. FLC uses heuristic information or expert operator knowledge to create its mechanism. However, nonlinearities exist in the control systems make its rule-based FLC design a non-trivial task. Manual trial and error tuning method is common way used for tuning the parameters of FLC to achieve the desired performance of the system. Hence, an automatic tuning using optimization algorithm is necessary. Meta-heuristic optimization algorithms are applied to tune the parameters of FLC automatically. In this paper, three popular meta-heuristic algorithms are used to optimize the scaling factors of FLC, i.e. Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Differential Evolution (DE) to improve the effectiveness of FLC design. A gantry crane system, which is a nonlinear system, is used as test system for this project. The simulation of the optimization of FLC parameters by using meta-heuristics algorithms was successfully achieved. The simulation results show that the optimization techniques for FLC are effective to improve the performance of conventional FLC for gantry crane control, i.e. position control and anti-swing control. In addition, PSO performs better than CS and DE in optimization of FLC for gantry crane system.

Quadrotor Tracking Control Based on Optimized Fuzzy Logic Controller

A quadrotor is required by the military to conduct surveillance and monitor in the border and coastal areas of Indonesia. To do so, it requires a tracking path. The widely used tracking control to follow the path is fuzzy logic controller algorithm. This algorithm is applied to control the quadrotor when it follows the trajectory. The problem is that it takes a long rise time; moreover, there is an error on the steady time which generates overshoot. The error member set of the fuzzy logic controller was optimised to obtain more optimum results. The proposed algorithm showed that the quadrotor moved on a predefined trajectory in 1.5 seconds rise time, and 1.9 seconds steady-state time, and there was no overshoot. The problem is that it takes long rise time, generates Error on the steady-state time and there is overshoot. The research aims to accelerate the quadrotor, remove the Error on the steady-state time and reduce overshoot. It optimises the Error member set of the fuzzy logic controller to obtain a more optimum result. The research result showed that the quadrotor moved on a predefined trajectory in 1.5 seconds rise time. The steady-state time of the quadrotor was reached at the 1.9th seconds. The proposed algorithm enables the quadrotor to perform faster rise time, remove Error on the steady time and generate minute overshoot that is 0.1.