Performance Of Fuzzy Logic Controller Research Articles

Fuzzy controlled modified reduced switch converter for switched reluctance motor under dynamic loading

In this paper, modified reduced switch converter topology is used to drive 8/6 pole, 7500 W switched reluctance motor (SRM) with an electric vehicle (EV) load. Fuzzy logic control (FLC) is developed for the modified converter topology and its performance is compared with the proportional integral (PI) controller. Analytical equations, switching pulses and different mode of operation are presented for modified reduced switch converter using double phase magnetization scheme. The converter topology adopts a modified switching sequence i.e., magnetization then freewheeling before demagnetization. It offers lesser torque ripples, reduced phase current and need only four switches for a 4-phase SRM drive. Modified reduced switch converter is simulated in MATLAB-simulation to investigate and compare the steady state waveforms and transient speed response of the PI and FLC. Torque ripple in modified converter is 50% less than the classical converter. Peak overshoot and settling time performance of FLC is superior as compared to PI, when applied to modified converter with EV loading.

Voltage stability of a photovoltaic DC microgrid using fuzzy logic controller

This article employs a fuzzy logic controller (FLC) to investigate voltage stability in a PV-based DC microgrid. Several photovoltaic (PV) modules, a DC-DC converter, and loads make up the microgrid. Due to the widespread use of intermittent PV power, voltage stability is a crucial problem for DC microgrids and is difficult to accomplish. This study proposes an FLC-based voltage control technique that leverages input factors including PV output power, DC-DC converter duty cycle, and load current to identify the best course of action for preserving the system's voltage stability. The FLC's performance is assessed by simulation, and it is meant to be resilient to parameter fluctuations and uncertainties. The simulation results demonstrate that the suggested FLC-based control strategy successfully maintains the microgrid's voltage stability under a variety of operational circumstances, including changing solar irradiance and load variations. Moreover, the FLC performs better than other control methods.

Vibration Control and Ride Comfort Analysis of a Full Car with a Driver Model Using Big-Bang Big-Crunch Optimized FLC

The main motive of the active suspension system is to enhance the ride comfort by suppressing the vibrations induced due to road irregularities. The actuator is getting more significance in the automotive industry due to its superior ride comfort for passengers. In this study, an eight Degrees of Freedom (DOF) full car with a driver model is considered for the ride comfort analysis (ISO2631 Standard) and vibration control. The real road terrain is not uniform in nature. Hence an effective intelligent control technology is required to improve the quality of travel. Initially, a conventional Proportional Integral and Derivative (PID) controller is designed for the system. Then a Fuzzy Logic Controller (FLC) is designed for the 8 DOF vehicle model to control the unwanted vibration produced in the vertical and angular directions. The FLC is designed with suitable firing rules, membership functions, and scaling factors using MATLAB/Simulink 2019 environment. The performance of FLC is further enhanced by the Big Bang Big Crunch (B3C) optimization technique, and it can suppress vibrations when the vehicle is travelling over bumpy and imperfect random road terrain as per ISO 8608. The Root Mean Square (RMS), Frequency Weighted RMS (FWRMS), and Vibration Dose Value of seat acceleration are the performance indices to investigate the potency of B3C-tuned FLC against the PID controller, FLC and passive suspension system.

Implementation and Analysis of a Fuzzy Logic and Sliding Mode Controller on a Boost DC/DC Converter in a PV Array

The DC/DC converters are primarily and practically used for switch-mode regulated power source and renewable energies such as photovoltaic or wind turbine. the principal purpose of these converters is to adapt the input with the output in other words, to preserve a consistent output voltage no matter the variations of the internal and external parameters of the converter. Different control techniques are commonly used to adapt and regulate the output voltage of these converters and make them more efficient and more robust in the event of unwanted disturbances, such as classic linear controllers for instance (proportional integrator and derivator controller PIDC ) and nonlinear controllers as (fuzzy logic FLC, sliding mode controller SMC). This article presents a comparative performance of Fuzzy Logic Control (FLC) and Sliding Mode Control (SMC) on a DC/DC Boost converter submitted to different type of variations. The performance evaluation criteria depend on speed and precision of the transient response. The two proposed controllers are modeled, designed and simulated using MATLAB/SIMULINK. The results of the comparison between the two controllers confirm the effectiveness of sliding mode control in terms of rapidity and precision compared with FLC.

Fermat-curve based fuzzy inference system for the fuzzy logic controller performance optimization in load frequency control application

Fermat-curve based fuzzy inference system for the fuzzy logic controller performance optimization in load frequency control application

Optimal Battery Management System Utilized in Electric Vehicle Using Fuzzy Logic Controller (FLC) and Artificial Eco-System Algorithm (AEO)

In real life applications, using electronics switch in high voltage and high current applications is very challenging due to limitation of hardware and safety. In this manuscript, a hybrid system is proposed for battery management system (BMS) used in electric vehicle (EV) is a critical part for effective utilization of the energy storage. The proposed hybrid technique is joint performance of fuzzy logic controller (FLC) and Artificial eco-system algorithm (AEO), hence it is called FLTAEO technique. The existing battery management control system is not effective for the Induction motor drive based solar PV powered electric vehicle system. In this manuscript, the control strategies of charging with discharging of Li-ion batteries coupled Induction motor with solar PV power are used with proposed technique. Here, the introduced FLC and AEO are discussed with detailed control abilities for charging/discharging. The simulations are done in MATLAB platform to analyze the superiority of the proposed method. Here, BESS life comparison, fitness function for charging, fitness function for discharging, error Distribution, RMSE calculation for 1 hour and 5 minutes ahead prediction interval (PI) are analyzed and compared with various existing techniques, like Emperor Penguin Algorithm (EPA) and Seagull Optimization Algorithm (SOA).

Investigation of performance of fuzzy logic controllers optimized with the hybrid genetic-gravitational search algorithm for PMSM speed control

Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer’s adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA.

Fuzzy logic controller based ship navigation system

This research work deals with more proficient and at ease to design the fuzzy logic controller (FLC) meant for course-changing. A new system in tuning a rule base in fuzzy controller system. Agreeing to the modification status of system error, the proposed real-time tuning algorithm is used in tuning the subsequent portion of fuzzy values, later the rule base is controlled. This proposed work also links the more performance of FLC at several working situations with conventional PID controller. In our proposed method we joined the PID with Fuzzy logic to build the strong and adaptive controller. The Simulation results are valuable to demonstrate this method.

Fuzzy Logic-Based Duty Cycle Controller for the Energy Management System of Hybrid Electric Vehicles with Hybrid Energy Storage System

Due to increasing fuel prices, the world is moving towards the use of hybrid electric vehicles (HEVs) because they are environmentally friendly, require less maintenance, and are a green technology. The energy management system (EMS) plays an important role in HEVs for the efficient storage of energy and control of the power flow mechanism. This paper deals with the design, modeling, and result-oriented approach for the development of EMS for HEVs using a fuzzy logic controller (FLC). Batteries and supercapacitors (SCs) are used as primary and secondary energy storage systems (ESSs), respectively. EMS consists of the ultra-power transfer algorithm (UPTA) and FLC techniques, which are used to control the power flow. The UPTA technique is used to charge the battery with the help of a single-ended primary inductor converter (SEPIC) during regenerative braking mode. The proposed research examines and compares the performance of FLC with a proportional integral (PI) controller by using MATLAB (Simulink) software. Three scenarios are built to confirm the efficiency of the proposed design. The simulation results show that the proposed design with FLC has a better response as its rise time (2.6 m) and settling time (1.47 µs) are superior to the PI controller.

Optimizing the Performance of Fuzzy Logic Controller in Power Quality Enhancement

Optimizing the Performance of Fuzzy Logic Controller in Power Quality Enhancement

Optimal extraction of photovoltaic energy using fuzzy logic control for maximum power point tracking technique

In photovoltaic (PV) systems, maximum power point tracking (MPPT) techniques are used to track the maximum power from the PV array under the change in irradiance and temperature conditions. The perturb and observe (P&O) is one of the most widely used MPPT techniques in recent times due to its simple implementation and improved performance. However, the P&O has limitations such as oscillation around the MPP during which time the P&O algorithm will become confused due to rapidly changing atmospheric conditions. To overcome the above limitation, this paper uses the fuzzy logic controller (FLC) to track the maximum power from the PV system under different irradiance, integrates it with a DC-DC boost converter as a tracker. The result of the FLC performance is compared with the traditional P&O method and shows the MPPT algorithm based on FLC ensures continuous tracking of the maximum power within a short period compared with the traditional P&O method. Besides that, the proposed method (FLC) has a faster dynamic response and low oscillations at the operating point around the MPP under steady-state conditions and dynamic change in irradiance.

Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement

Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.

AN FLC-PSO ALGORITHM-CONTROLLED MOBILE ROBOT

The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.

Optimization of Threshing Quality Control Strategy Based on Type-2 Fuzzy Logic Controller

At present, loss rate and grain damage are the most complicated problems for combine harvester. Besides, existing controllers that control threshing quality do not take into account environmental noise, data fluctuations, and other detection factors. In this paper, considering the interference of uncertain factors, a control strategy with low crashed rate and reduced loss rate using type-2 Fuzzy Logic Controller (FLC) is proposed. This control strategy takes threshing separation component as a research object and adopts combination of theory with experimental analysis. Firstly, basic data acquisition experiment of threshing system is designed. Secondly, a control strategy is established by adequately previous filled test data. In the process of control strategy design, Fuzzy Logic Toolbox evaluates the performance of type-2 FLC. Integral of Square Error (ISE) and Integral of the Absolute value of the Error (IAE) show a better performance of type-2 FLC than of type-1 FLC. At last, field experiments are designed to verify the effectiveness of type-2 control strategy. The field test shows that the maximum reduction of grain damage rate and loss rate can reach to 44.08 % and 29.6 %. Experiment results show that the type-2 FLC can significantly reduce loss rate, crashed rate, and improve threshing quality.

MPPT for Single Phase Five Level Quasi-z-source Photovoltaic Inverter with Fuzzy Controller

The fast growth of PV system has led to the development of power electronics devices which come along with lots of challenges. One of these is the structure design including the choice of different elements involved. This paper presents an advanced off grid photovoltaic (PV) system with less components and high efficiency. The structure shows a multilevel cascaded inverter (CHB) with the advantage of less harmonics and high voltage level, quasi-z source inverter (Q-ZSI) which plays the buck/boost function for efficient operation of power electronic devices. Hence, reducing the cost and the volume of the whole system. The buck/boost operation is implemented by simple boost control (SBC) method for shoot through insertion. The pulse width modulation technique (SPWM) used is phase shift technique (PS-SPWM). This paper also compares the performance of fuzzy logic controller(FLC) with incremental conductance (Inc cond) algorithm for MPPT by focussing on their adaptability, speediness and precision despite the changes in solar irradiance. The overall system is implemented in matlab-simulink environment for simulation. Thereby confirming the feasibility and efficiency of the system.

Detailed implementation of asynchronous circuits on commercial FPGAs

This paper provides the essential details of implementing 4-phase bundled data and speed independent asynchronous circuits on FPGAs. The required Xilinx synthesis tools including attributes, constraints and hardware implementation of basic asynchronous elements like Cgate, delay line, and handshaking modules are discussed. Finally, two design and implementation examples of asynchronous circuits are introduced. In order to reduce area and energy overhead, an N-stage pipeline with internal loops is proposed and employed in asynchronous Fuzzy Logic Controller (FLC). It is observed that synchronous FLC operating at 100 MHz consumes 27% more dynamic power while occupying 23% fewer FPGA resources compared to its asynchronous counterpart. At the same time, the asynchronous circuit has obtained an improvement of 19% in FLC performance compared to synchronous FLC. The other implementation example explains the technical details of the design and implementation process of speed independent circuit using Petrify and ISE at the LUT level. Both design examples are implemented and tested successfully on FPGA board.

Design of travel angle control of quanser bench-top helicopter using mamdani-based fuzzy logic controller

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">This research focuses on travel angle control of a laboratory scale bench-top helicopter developed by Quanser Inc. Bench top-helicopter is usually used by engineers and researchers to test their designed controllers before applying to the actual helicopter. Bench-top helicopter has the same behavior as the real helicopter, with 3 degree of freedom. The bench-top helicopter is mounted on a flat surface with two rotors that depends on the voltage supplied to change the direction of the helicopter in 3 different angles. The movement of the helicopter is based on the direction of three-different angles; travel, pitch and yaw angles. The existing Linear Quadratic Regulator-Integral controller used by Quanser Inc has some limitations in terms of tracking capability and settling time; therefore this research is proposed. The objective of this research is to develop Mamdani-based Fuzzy Logic Controller for travel angle control of bench-top helicopter. Performance comparison has been done with the existing Linear Quadratic Regulator-Integral controller in both simulation and hardware. From the test results, it was found that the performance of Fuzzy Logic Controller is better than LQR-I controller especially for closed-loop simulation at desired angle of 30°. The percentage of overshoot of the Fuzzy Logic Controller has been improved from the existing controller which is 4.912% compared to 7.002% for LQR-I.</span>

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.

COMPARISON OF CONTROLLER PERFORMANCE FOR THE ANALYSIS OF SUPERFICIAL CANCER THERAPY

The primary objective of the research work presented is to design a thermal dose controller for hyperthermia treatment using several soft computing techniques, perform simulation studies on the mathematical model of the carcinogenic tissue and compare the controller performance. The non-linear relationship between temperature and dosage, after effects of treatment and constraints on normal tissue protection makes design of thermal dose controller a challenging task. Intelligent control of thermal dose using soft computing techniques like Fuzzy Logic Controller (FLC) and Fuzzy Adaptive Learning Control Network (FALCON) has been simulated on the tissue model and the performance of the controllers are compared in terms of accuracy of output and patient comfort. In the proposed system, optimum thermal dose to which the carcinogenic tissue can be exposed is determined. State space representation of the thermal model of the tissue is used for simulation studies. The performance metrics are the transient response characteristics like rise time, settling time and mean square error. These characteristics are compared to assess the controller performance. The FLC and FALCON performance for both linear and exponential parametrization has been compared. It has been observed that implementation of Fuzzy Controller and FALCON for thermal dose is successful in minimizing the treatment time. Model validation has been done by observing the mean square error which is well within the permissible limits. The concept of Automatic thermal dose controller has been enhanced using hybrid Neuro-fuzzy Controller (FALCON). The developed controller is applicable for a wide range of thermal therapies like thermal ablation and thermal radiotherapy.

Design and Implementation of Controller for PH Process At Elevated Pressure

This paper describes the modeling and control of a pH neutralization process and compares the traditional, fuzzy logic and Genetic Algorithm (GA) optimization methods for the novel deep sea microbial instrument at elevated pressure. National Institute of Ocean Technology (NIOT) has designed, developed and patented a novel instrument to mimic deep sea conditions in laboratory for deep sea microbial exploration. Controlling pH in the novel deep sea conditions mimicking laboratory system is complicated, because of high salinity, temperature stimulus, high pressure operation, and its non-linearity. To address the pH control issues a systematic real time experimental model was designed developed, implemented and analyzed. The simulation results shows that the proposed controller technique is effective in tracking set point and has resulted in a minimum value of the Integral Square Error, peak overshoot and minimum settling time as compared to conventional methods. The experimental results show that the model accuracy and the GA and fuzzy logic controller performance is superior then the other control methods and it matches favorably with the simulation results.