Fuzzy Logic-based Control Algorithm Research Articles

Optimizing Blood Glucose Regulation in Type 1 Diabetes Patients via Genetic Algorithm-Based Fuzzy Logic Controller Considering Substantial Meal Protocol

Effective management of blood glucose levels in individuals with type 1 diabetes, especially after meals, is crucial for diabetes care. Artificial pancreas systems (APS) perform automated insulin delivery in subjects with type 1 diabetes mellitus (T1DM). In this study, an optimized fuzzy logic controller was designed to achieve a euglycemic range after a substantial meal intake. All in silico simulations were performed using the MATLAB/Simulink environment, leveraging control variability grid analysis (CVGA), and the performance of the controller was evaluated. The proposed controller is based on a fuzzy-logic control law designed in three stages. First, a nonlinear framework of the glucose-insulin regulatory system was identified based on the heavy meal protocol of three patients given as follows: for subject ID 117-1, a total of 295 gCHO; for subject ID 126-1, 236 gCHO; and subject ID 128-1, 394 gCHO over a day. Then, an iterative tree structure was employed to establish a stabilizing control rule for insulin delivery, integrating inputs from two Mamdani Fuzzy Inference System (FIS) objects. Finally, a genetic algorithm refines the control system by fine-tuning the uncertainty of the fuzzy membership functions. Two scenarios were considered for three patients to assess the performance of the proposed controller. The results indicated its effectiveness under various conditions, achieving a time in the range of 61.25%, 71% and 61.10% respectively for the three subjects. The obtained results are analyzed and compared with IMC and multi-objective output feedback controllers. The findings of the study reveal that the proposed controller shows promising advancements in tailored strategies for type 1 diabetes patients, outperforming the other controllers in terms of blood glucose regulation.

Fuzzy logic-based Energy Management System (EMS) of hybrid power sources: Battery/Super capacitor for electric scooter supply

The use of electric motors instead of internal combustion engines has been increasing in the automotive industry, particularly over the past decade. Studies attempting to use electrical energy as the only source to obtain the required movement from the wheels have run into difficulties due to the limitations of electricity storage units. Li-ion batteries as a power source have shown some interesting characteristics, especially in electric vehicles, but there is still research for development in the storage device. Findings from this research recommend combining battery and supercapacitor technology to power an electric scooter. Both of these power sources are modulated for maximum efficiency in transferring energy to the motors. For this reason, a fuzzy logic-based control algorithm for energy management is developed to keep the battery and supercapacitor at their optimal states of charge (SOC), meet load demands during braking and counter-loading, and boost the energy system's independence. An electric scooter's performance under varying loads is simulated in MATLAB and Simulink to verify the effectiveness of the EMS. The simulation results findings validate the usefulness of the proposed approach and present the novel EMS as very good solution for energy management problem using the fuzzy logic expert system to take decision.

An adaptive fuzzy logic control technique for LVRT enhancement of a grid-integrated DFIG-based wind energy conversion system

The fast improvement of wind energy conversion technology with optimization algorithms has recently received a lot of attention. However, their slow convergence speed, huge computational load, and low efficiency are the main drawbacks and to improve these disadvantages, a new adaptive fuzzy logic controller strategy is proposed to enhance the low voltage ride-through of the grid-connected doubly fed induction generator during severe grid faults. The rotor side converter and the grid side converter are controlled using an adaptive fuzzy logic controller topology under cascaded structure to improve the performance. The novel application of the ‘generalized variable step-size diffusion continuous mixed p-norm’ adaptive filtering algorithm is proposed to modify the calibrating factors of the fuzzy logic controllers at a rapid convergence speed with low normalized misalignment error. The proposed adaptive algorithm-based fuzzy logic controller has a better low voltage ride-through improvement capability than that of using the particle swarm optimization-based proportional-integral controller during severe grid disturbances. The convergence speed of the proposed adaptive filtering algorithm is compared to that of existing algorithms such as least mean fourth, least mean square, and continue mixed p-norm algorithms. In addition, a comparison is also made with different common optimization methods. The field-programmable gate array-based real-time experimental results are presented to validate the proposed adaptive control topology.

RETRACTED: Novel transient stability analysis of grid connected hybrid wind/PV system using UPFC

In transmission line, the series compensation is used to improve stability and increases the power transmission capacity. It generates sub synchronous resonance (SSR) at turbine-generator shaft due to the interaction between the series compensation and wind turbine system. To solve this, several methods have been presented. However, these provide less performance during contingency period. Therefore, to mitigate the SSR and also to improve the dynamic performance of hybrid wind and PV system connected with series compensated wind farms, the adaptive technique of the Black Widow Optimization algorithm based Fuzzy Logic Controller (BWO-FLC) with UPFC is proposed in this paper. Here, the objective function is solved optimally using BWO technique. Based on this, the Fuzzy Logic Controller is designed. The results proved that the proposed controller performs the mitigation of SSR. The damping ratios of proposed controller to mitigation of SSR are 0.0098, 0.0139, and 0.0195 for wind speed of 6, 8 and 10 m/s respectively.

A Fuzzy Logic-Based Control Algorithm for the Recharge/V2G of a Nine-Phase Integrated On-Board Battery Charger

Energy demand associated with the ever-increasing penetration of electric vehicles on worldwide roads is set to rise exponentially in the coming years. The fact that more and more vehicles will be connected to the electricity network will offer greater advantages to the network operators, as the presence of an on-board battery of discrete capacity will be able to support a whole series of ancillary services or smart energy management. To allow this, the vehicle must be equipped with a bidirectional full power charger, which will allow not only recharging but also the supply of energy to the network, playing an active role as a distributed energy resource. To manage recharge and vehicle-to-grid (V2G) operations, the charger has to be more complex and has to require a fast and effective control structure. In this work, we present a control strategy for an integrated on-board battery charger with a nine-phase electric machine. The control scheme integrates a fuzzy logic controller within a voltage-oriented control strategy. The control has been implemented and simulated in Simulink. The results show how the voltage on the DC-bus is controlled to the reference value by the fuzzy controller and how the CC/CV charging mode of the battery is possible, using different charging/discharging current levels. This allows both three-phase fast charge and V2G operations with fast control response time, without causing relevant distortion grid-side (Total Harmonic Distortion is maintained around 3%), even in the presence of imbalances of the machine, and with very low ripple stress on the battery current/voltage.

Grasshopper Optimization Algorithm-Based Fuzzy Logic Controller for Performance Enhancement of a Grid-Connected Wind Generator

Due to the direction of the world to diminish pollution production and to enhance the environmental conditions, the penetration of wind energy in the electric grid is appreciably increased nowadays. The variable-speed wind turbine direct-drive permanent-magnet synchronous generator is connected to the electrical network through a frequency converter-based an optimum controller, such as a conventional proportional-integral controller (PI) or the fuzzy logic controller (FLC). The PI has a constant gain but has some drawbacks, such as affected by variable variation. This paper exhibits an implementation of a grasshopper optimization algorithm (GOA) to design FLC. GOA-based FLC is utilized to control the grid- and generator-side converters to enhance the transient stability of the grid tied-wind energy. To ensure the efficacy of the proposed control scheme, a comparison among the results achieved by the GOA-based FLC and Newton Raphson based-PI has occurred. The simulation results describe the capability of the GOA-based FLC to enhance the response of the grid-tied wind energy under various operation cases. Simulation is carried out by MATLAB/Simulink program.

Use of the Genetic Algorithm-Based Fuzzy Logic Controller for Load-Frequency Control in a Two Area Interconnected Power System

The use of renewable energy resources has created some problems for power systems. One of the most important of these is load frequency control (LFC). In this study, in order to solve the LFC problem, modern control methods were applied to a two area multi source interconnected power system. A photovoltaic solar power plant (PV-SPP) was also connected, in order to identify the harmful effects on the frequency of the system. A new Genetic-based Fuzzy Logic (GA-FL) controller was designed to control the frequency of the system. For comparison, conventional proportional-integral-derivative (PID), fuzzy logic (FL), and Genetic Algorithm (GA)-PID controllers were also designed. The new control method exhibited a better performance than the conventional and other modern control methods, because of the low overshoot and short settling time. All simulations were realized with the Matlab-Simulink program.

Structural Analysis and Five-Layered Control Frame Work for Underground Pipe Inspection Robot

Design of a robot generally involves various types of analyses that invariably include structural, numerical, kinematic, dynamic and experimental analysis. A robot designed as a solution for social problems would have to undergo all these stages before it is incorporated in-situ. The authors in continuation to their earlier work [1] are presenting the results of structural analysis in this paper. Initially a detailed description of the anatomy of the proposed robot is provided for clarity along with the identified design parameters. The primary design parameter ‘load to power ratio’ establishes certain geometric and structural constrains on the design of links comprising the robot. This relationship is studied in order to improve to the load to power ratio of the robot. A significant reduction in weight is achieved by working on these variables which leads to the improvement in the functional aspects too, viz., reduced torque requirement from the motor. Upon finalizing on the choice of motors and sensors the 5 layered control architecture based on statespace representation is also presented. Fuzzy logic based control algorithm is proposed to unite the layered approach depending on the task allocated to the robot.

An Educational Tool for the Genetic Algorithm-Based Fuzzy Logic Controller of a Permanent Magnet Synchronous Motor Drive

In this study, a novel educational tool is introduced for the genetic algorithm (GA)-based fuzzy logic controller (FLC) of a permanent magnet synchronous motor (PMSM) drive. This tool provides the operating principle of PMSM, essentials of GAs, and effects of FLC parameters on the performance of GA-based FLC. The proposed educational tool regarding GA-based FLC is a unique one in its field in the literature, as such a computer-based study aiming to teach the corresponding intelligent system has not been studied before, though both GA and FLC are well-known powerful techniques. The graphical interface, developed in Microsoft Visual C#. NET platform, has a flexible and functional structure. By changing motor operating conditions as well as GA and controller parameters, useful practices can be done on the whole system under various situations without wasting time. An evaluation of the tool is given based on student feedback.

Genetic Algorithm Based Fuzzy Logic Controller for Wire-EDM Machine

Wire tension as well as wire feed should be controlled tightly for the geometry and corner accuracy of wire-EDM. A closed-loop wire tension control system for Micro-Wire-EDM is presented to guarantee a smooth wire transport and a constant tension value. To keep smooth wire transportation and avoid wire breakage during wire feeding, the reel roller is modified and the clip reel is removed from the wire transport mechanism. A genetic algorithm-based fuzzy logic controller (FLC) is proposed to investigate the dynamic performance of the closed-loop wire tension control system. Theoretical analysis and experimental research demonstrate that the developed wire transport system can result in satisfactory transient response, steady-state response and robustness. The proposed FLC can obtain faster transient response and smaller steady-state error than PI controller.

Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators

The paper presents a diagnostic algorithm for classifying cardiac tachyarrhythmias for implantable cardioverter defibrillators (ICDs). The main aim was to develop an algorithm that could reduce the rate of occurrence of inappropriate therapies, which are often observed in existing ICDs. To achieve low energy consumption, which is a critical factor for implantable medical devices, very low computational complexity of the algorithm was crucial. The study describes and validates such an algorithm and estimates its clinical value. The algorithm was based on the heart rate variability (HRV) analysis. The input data for our algorithm were: RR-interval (I), as extracted from raw intracardiac electrogram (EGM), and in addition two other features of HRV called here onset (ONS) and instability (INST). 6 diagnostic categories were considered: ventricular fibrillation (VF), ventricular tachycardia (VT), sinus tachycardia (ST), detection artifacts and irregularities (including extrasystoles) (DAI), atrial tachyarrhythmias (ATF) and no tachycardia (i.e. normal sinus rhythm) (NT). The initial set of fuzzy rules based on the distributions of I, ONS and INST in the 6 categories was optimized by means of a software tool for automatic rule assessment using simulated annealing. A training data set with 74 EGM recordings was used during optimization, and the algorithm was validated with a validation data set with 58 EGM recordings. Real life recordings stored in defibrillator memories were used. Additionally the algorithm was tested on 2 sets of recordings from the PhysioBank databases: MIT-BIH Arrhythmia Database and MIT-BIH Supraventricular Arrhythmia Database. A custom CMOS integrated circuit implementing the diagnostic algorithm was designed in order to estimate the power consumption. A dedicated Web site, which provides public online access to the algorithm, has been created and is available for testing it. The total number of events in our training and validation sets was 132. In total 57 shocks and 28 antitachycardia pacing (ATP) therapies were delivered by ICDs. 25 out of 57 shocks were unjustified: 7 for ST, 12 for DAI, 6 for ATF. Our fuzzy rule-based diagnostic algorithm correctly recognized all episodes of VF and VT, except for one case where VT was recognized as VF. In four cases short lasting, spontaneously ending VT episodes were not detected (in these cases no therapy was needed and they were not detected by ICDs either). In other words, a fuzzy logic algorithm driven ICD would deliver one unjustified shock and deliver correct therapies in all other cases. In the tests, no adjustments of our algorithm to individual patients were needed. The sensitivity and specificity calculated from the results were 100% and 98%, respectively. In 126 ECG recordings from PhysioBank (about 30min each) our algorithm incorrectly detected 4 episodes of VT, which should rather be classified as fast supraventricular tachycardias. The estimated power consumption of the dedicated integrated circuit implementing the algorithm was below 120nW. The paper presents a fuzzy logic-based control algorithm for ICD. Its main advantages are: simplicity and ability to decrease the rate of occurrence of inappropriate therapies. The algorithm can work in real time (i.e. update the diagnosis after every RR-interval) with very limited computational resources.

LQR-퍼지논리제어기에 의한 2중 차량 구조 역진자 시스템의 제어

임의의 비선형 시스템을 제어하기 위한 새로운 방법의 제어 알고리즘이 널리 보고되고 있으며, 그 유용성을 입증하기 위한 제어 대상 시스템으로 역진자 시스템이 널리 활용되고 있다. 본 논문에서는 스프링으로 연결된 2개의 차량에 장착된 역진자를 제어하는 알고리즘을 제시한다. 여기서 두 개의 차량 중 하나는 구동용, 다른 하나는 역진자를 장착한 무구동용이다. 이를 위한 시스템 모델링을 제시하고, 퍼지논리제어 시스템 기반의 양질의 제어기 설계를 제안한다. 본 논문에서는 퍼지논리제어기의 입력변수로 사용될 6개의 변수를 2개로 축소하기 위하여 LQR(Linar Quadratic Regulator) 기법을 도입하며, 이를 통하여 퍼지논리제어기 설계의 복잡성을 줄일 수 있음을 보인다. 더욱이 개선된 2-입력 퍼지논리제어기의 제어 규칙표가 skew-symmetric의 특성을 가지는 성질로부터 다시 단일입력 퍼지논리제어기 설계를 제안한다. 제안한 방법의 유용성을 입증하기 위하여 시뮬레이션을 수행하며, 이를 통하여 제안한 방법의 우수성을 입증한다. Any new method for controlling a nonlinear system has widely been reported. An inverted pendulum system has typically been used as a target system for demonstrating its usefulness. In this paper, we propose an algorithm to control a flexible joint cart based inverted pendulum system. Two carts are connected with a spring and one is a driving cart and the other is no driving cart with a pole. We here present a system modeling and a good fuzzy logic based control algorithm. We also introduce LQR (Linar Quadratic Regulator) technique for reducing the number of control variables. By using this technique, the number of input variables for a fuzzy logic controller is become only two not six. So the computational complexity is largely reduced. Moreover, a two-input fuzzy logic controller has a control rule table with a skew-symmetric property. And it will lead the design of a single-input fuzzy logic controller. In order to demonstrate the usefulness of the proposed method and prove the superiority of the proposed method, some computer simulations are presented.

Development of genetic algorithm-based fuzzy logic controller for conical tank process

The proportional integral derivative controllers are widely used in industries for controlling the different process variables due to its simplicity, flexibility and efficiency. Recently, the control of non-linear processes in the industries have turned the attention towards the intelligent controllers such as neural networks, fuzzy logic controller (FLC), genetic algorithm-(GA) tuned controllers, adaptive controller, predictive controller, robust controller, etc. This work focuses on developing a GA-based FLC for conical tank. A conical tank is a highly non-linear process due to the variation in the area of cross section of the level system with change in shape. Conventionally, a parameter adaptive proportional integral (PI) controller has been designed for non-linear process. Alternatively, in this work, an intelligent controller (GA-based FLC) is designed for the control of non-linear process to ensure the exact level control. The experimental results are obtained for servo and regulatory response of the process. The GA-based FLC is compared with adaptive PI controller.

Hybrid active mass damper (AMD) vibration suppression of nonlinear high-rise structure using fuzzy logic control algorithm under earthquake excitations

Active mass damper (AMD) control of civil structures has attracted much attention in recent years. However, there are few researches on the control of nonlinear structure using AMD under earthquake excitation, especially for high-rise building. In this paper, a fuzzy logic-based control algorithm was developed to control a nonlinear high-rise structure under earthquake excitation using AMD device. For the control of a high-rise building under earthquake excitation, due to the complexity of vibration modes, many inputs are needed for the fuzzy controller, and this may cause the explosion of fuzzy rules. To solve this problem, generalized inputs for fuzzy control were proposed by considering all the states of the structure based on a quadratic performance index. An AMD control system may magnify the interstory drift of a high-rise nonlinear building, which is regarded as Interstory Response Amplification (IRA) phenomenon. To avoid this problem, a control scheme of using both an AMD and interstory dampers was proposed to control the nonlinear vibration of a high-rise structure. We used a 20-story Benchmark nonlinear structure for the numerical studies. Numerical simulations show that the combination of an AMD and interstory dampers can eliminate the IRA phenomenon and achieve better vibration suppression than either an AMD alone or interstory dampers alone, in not only displacements relative to ground but also interstory response. Copyright © 2010 John Wiley & Sons, Ltd.

Modeling and analysis of an FC/UC hybrid vehicular power system using a wavelet-fuzzy logic based load sharing and control algorithm

Abstract Fuel cell (FC) systems are potentially promising candidates as alternative energy sources for use in vehicular applications. The natural advantages of hybrid power sources may be effectively utilized to improve the efficiency and dynamic response of a vehicular system. Fuel cell (FC) and ultra-capacitor (UC) based hybrid power systems appear to be very promising for satisfying high energy and high power requirements for vehicular applications. In this paper, a FC/UC hybrid vehicular power system using a wavelet based load sharing and fuzzy logic based control algorithm is proposed. While wavelet transforms are suitable for analyzing and evaluating the dynamic load demand profile of a hybrid electric vehicle (HEV), the use of fuzzy logic controller is appropriate for the hybrid system control. The mathematical and electrical models of the hybrid vehicular system are developed in detail and simulated using MATLAB®, Simulink® and SimPowerSystems® environments.

Application of genetic algorithm-based fuzzy logic control in wire transport system of wire-EDM machine

Wire tension as well as wire feed should be controlled tightly for the geometry and corner accuracy of wire-EDM. In this paper, a closed-loop wire tension control system for Micro-Wire-EDM is presented to guarantee a smooth wire transport and a constant tension value. In order to keep smooth wire transportation and avoid wire breakage during wire feeding, the reel roller is modified and the clip reel is removed from the wire transport mechanism. A genetic algorithm-based fuzzy logic controller is proposed to investigate the dynamic performance of the closed-loop wire tension control system. Experimental results demonstrate that the developed wire transport system can result in satisfactory transient response, steady-state response and robustness. The proposed genetic algorithm-based fuzzy logic controller can obtain faster transient response and smaller steady-state error than a PI controller.

Harmonic Reducing ANN Controller for a SVC Compensating Unbalanced Fluctuating Loads

This paper presents the development of an artificial neural network (ANN) controller for a Static Var Compensator (SVC) system compensating unbalanced fluctuating loads. The proposed controller can balance and reduce the reactive power drawn from the source under unbalanced loads while keeping harmonic injection to the system due to SVC operation low. The first stage of controller development is a fuzzy logic based control algorithm. This algorithm determines a number of possible operating states of SVC for a given load condition and calculates corresponding harmonics injections. Then a fuzzy logic system is used to rank those operating states in terms of the magnitude of the reactive power drawn from the source and the harmonic injection level indicated by Total Demand Distortion (TDD). The best operating state is selected based on the ranking score assigned by the fuzzy logic ranking system. Finally, computational speed of the controller is improved by replacing the analytical and fuzzy computations by a set of neural networks trained with data generated with fuzzy logic based controller.

Nonlinear linearization controller and genetic algorithm-based fuzzy logic controller for ABS systems and their comparison

This paper addresses the design and performance of nonlinear controllers for Anti-lock Braking Systems (ABS). The controllers, based on a nonlinear feedback linearization scheme and fuzzy logic control strategies, are designed to solve the wheel slip-ratio tracking problem of a simplified quartercar ABS model, respectively. Given the desired (optimal) wheel slip-ratio, the feedback linearizing controller cancels all nonlinear dynamics and imposes an appropriate linear behaviour on the wheel slip-ratio such that it will track in a desired way. Using the slip-ratio error and the change-in-error measurements, fuzzy logic controllers have also been developed to resolve the wheel slip tracking problem. In contrast to conventional fuzzy logic control systems, a Genetic Algorithm (GA)-based fuzzy logic controller is proposed to solve the same problem in which the explicit knowledge of parameters for the membership functions is deemed unnecessary. Numerical simulations demonstrate the performance of the proposed control schemes subjected to different road conditions and in the presence of variations in system parameters and a bounded control input. The results indicate that superior performance can be obtained using the GA-based fuzzy logic controller when compared with the feedback linearizing controller and the conventional fuzzy logic controller.