Fuzzy Logic Controller In Order Research Articles

Direct torque control and dynamic performance of induction motor using fractional order fuzzy logic controller

<p><span lang="EN-US">Conventional direct torque control (DTC) is one of the best control systems for regulating the torque of an induction motor (IM). However, the DTC’s enormous waves in flux and torque cause acoustic noise that degrades control performance, especially at low speeds due to the DTC’s low switching frequency. Direct torque control systems, which focus just on torque and flux, have been proposed as a solution to these problems. In order to improve DTC control performance, this work introduces a fractional-order fuzzy logic controller method. The objective is to analyze this technique critically with regard to its efficacy in reducing ripple, its tracking speed, its switching loss, its algorithm complexity, and its sensitivity to its parameters. Simulation in MATLAB/Simulink verifies the anticipated control approach’s performance.</span></p>

Optimum of fractional order fuzzy logic controller with several evolutionary optimization algorithms for inverted pendulum

AbstractThis paper compared the performance between Integer Order Fuzzy PID (IOFPID) and Fractional Order Fuzzy PID (FOFPID) controllers for inverted pendulum system as a controlling plant. The parameters of each controller were tuned with four evolutionary optimization algorithms (Social Spider Optimization (SSO), Swarm Optimization (PSO), Genetic Algorithm (GA), and Particle Ant Colony Optimization (ACO)). The comparisons were carried out between the two controllers IOFPID and FOFPID, as well as among the four optimization algorithms for the two controllers. The results of comparisons proved that the FOFPID controller with SSO has achieved the best time response characteristics and the least tuning time.

A preclinical model of post-surgery secondary bone healing for subtrochanteric femoral fracture based on fuzzy interpretations.

Mechanobiology plays an essential role in secondary bone fracture healing. While the introduction of newer type of plates, e.g. locking plate (LP), is becoming increasingly popular for complex femoral fractures, the conventional technique involving dynamic compression plate (DCP) remains the standard choice. The difference between the two techniques lies primarily in their screw fixation mechanisms. The present study applied 3D dynamic fracture healing scheme modelled on a subtrochanteric femur fracture, regulated by both finite element (FE) analysis and Fuzzy logic control in order to understand the spatio-temporal healing phenomena for both LP and DCP. The study further examined the influence of the two screw fixation mechanisms in determining the comparative progression of fracture healing. The problem was solved iteratively in several healing steps running in loop and accordingly, the local tissue concentrations and material properties were updated. The predicted results accorded well with various previous experimental observations. The study found an initial delay in healing associated with DCP. However, as the healing progressed, there was no significant difference in overall callus modulus. The presented preclinical model may further help predict bone healing for different implantation techniques, and thus can serve as a non-invasive tool for evaluating relative merits of extramedullary plating techniques.

New Modulation Technique in Smart Grid Interfaced Multilevel UPQC-PV Controlled via Fuzzy Logic Controller

The use of sensitive electronic devices has increased recently; hence, power quality has become an important factor in electrical power systems. The various disturbances occurring in the electrical network (harmonics, voltage swells, sags, etc.) may lead to technical-economic damage that negatively impacts the quality of the supplied power. Therefore, the use of the Unified Power Quality Conditioner (UPQC) is a promising solution to limit such damage. In this paper, a nine-level structure of the proposed standardized power quality conditioner is analyzed. The conditioner is connected between a photovoltaic system (UPQC-PV) and a smart grid. Then, a new approach to generate switches for the switches of both the series and parallel converters is proposed. This modulation technique relies on an adaptive hysteresis band (AHB) that is determined via a fuzzy logic controller in order to obtain the required modified output voltage with minimum distortion. Simulation results, using MATLAB/Simulink, of different disturbance scenarios that may occur in the network are presented to verify the accuracy of the proposed fuzzy-logic-based AHB control system. Compared with the conventional SPWM, it is found that the proposed AHB modulation technique significantly improves the power quality in terms of producing less total harmonic distortion in both the voltage and current waveforms.

Photovoltaic and Thermoelectric Generator Combined Hybrid Energy System with an Enhanced Maximum Power Point Tracking Technique for Higher Energy Conversion Efficiency

In this paper, the design and performance investigation of the hybrid photovoltaic–thermoelectric generator (PV–TEG) system with an enhanced fractional order fuzzy logic controller (FOFLC)-based maximum power point tracking (MPPT) technique is presented. A control strategy of the variable incremental conduction (INC) method is employed using FOFLC for the MPPT control technique to efficiently harvest the maximum power from the PV module. The fractional factor α used in the MPPT control algorithm is a supporting fuzzy logic controller (FLC) for the accurate tracking of the maximum power point (MPP) and to maintain the constant output after reaching the MPP. In the proposed system configuration, the TEG is mounted with the PV panel for generating the extra electrical power using the waste heat energy produced on the PV panel due to the incident solar irradiation. The PV and TEG are connected electrically in series to increase output voltage level and thereby improve the power output. The hybrid energy module has better energy conversion efficiency when compared to the standalone PV array. The performance of the proposed MPPT technique is studied for the PV–TEG hybrid energy module under various thermal and electrical operating conditions using a MATLAB software-based simulation. The results of the FOFLC-based MPPT technique are compared with the conventional perturb and observe (P&O) and FLC-based P&O methods. The proposed MPPT technique confirms its effectiveness in extracting the maximum power in terms of speed and accuracy. Moreover, the PV and TEG combined system provides higher energy efficiency than the individual PV module.

Power Efficiency Improvement of a Boost Converter Using a Coupled Inductor with a Fuzzy Logic Controller: Application to a Photovoltaic System

DC/DC converters are widely used in photovoltaic (PV) systems to track the maximum power points (MPP) of a photovoltaic generator (PVG). The variation of solar radiation (G) and PV cells temperature (T) affect the power efficiency of these DC/DC converters because they change the MPP, thus a sizing adaptation of the component values in these DC/DC converters is needed. Power loss in the inductor due to core saturation can severely degrade power efficiency. This paper proposes a new method that allows to adapt the inductor values according to the variable output power of the PV array in order to minimize losses and improve the converter power efficiency. The main idea is to replace the DC/DC inductor with a coupled inductor where the DC/DC inductor value is adjusted through an additional winding in the magnetic core that modulates the magnetic field inside it. Low current intensities from the PVG supply this winding through a circuit controlled by a fuzzy logic controller in order to regulate the second winding current intensity. Experimental results show a significant improvement of the power efficiency of the proposed solution as compared to a conventional converter.

A Variable Fractional Order Fuzzy Logic Control Based MPPT Technique for Improving Energy Conversion Efficiency of Thermoelectric Power Generator

Thermoelectric generation technology is considered to be one of the viable methods to convert waste heat energy directly into electricity. The utilization of this technology has been impeded due to low energy conversion efficiency. This paper aims to improve the energy conversion efficiency of the thermoelectric generator (TEG) model with a novel maximum power point tracking (MPPT) technique. A variable fractional order fuzzy logic controller (VFOFLC)-based MPPT technique is proposed in the present work in which the operating point of the TEG is moved quickly towards an optimal position to increase the energy harvesting. The fraction order term α, introduced in the MPPT algorithm, will expand or contract the input domain of the fuzzy logic controller (FLC to shorten the tracking time and maintain a steady-state output around the maximum power point (MPP). The performance of the proposed MPPT technique was verified with the TEG model by simulation using MATLAB /SIMULINK software. Then, the overall performance of the VFOFLC-based MPPT technique was analyzed and compared with Perturb and observe (P&O) and incremental resistance (INR)-based MPPT techniques. The obtained results confirm that the proposed MPPT technique can improve the energy conversion efficiency of the TEG by harvesting the maximum power within a shorter time and maintaining a steady-state output when compared to other techniques.

DESIGN AND APPLICATION OF SOLAR TRACKING SYSTEM USING OPTIMIZED FUZZY LOGIC CONTROLLER BY GENETIC ALGORITHM

This study describes an intelligent control algorithm for the solar tracking system (STS) providing maximum performance from the photovoltaic panel according to different sun positions. The solar tracking system is designed as dual axis to increase the efficiency of photovoltaic panels. DC motors are preferred in order to minimize cost and to control the azimuth and zenith angles in the solar tracking system. Fuzzy logic algorithms are used to adjust the speed of these motors to track the sun’s position with a high degree of accuracy. After designing a fuzzy logic controller in order to control the motors, membership functions of controller and control rules are simultaneously found by genetic algorithms which is an optimization algorithm based on natural selection and genetic mechanics. As a result in the study, the power performance analysis is compared between a photovoltaic panel positioned on the designed solar tracking system and a photovoltaic panel positioned on the static system. According to comparison results, the photovoltaic panel positioned on the solar tracking system is observed that it shows higher performance at varying rates of depending on the seasons.

Survey Study of Factional Order Controllers

This is a survey study that presents recent researches concerning factional controllers. It presents several types of fractional order controllers, which are extensions to their integer order counterparts. The fractional order PID controller has a dominant importance, so thirty-one paper are presented for this controller. The remaining types of controllers are presented according to the number of papers that handle them; they are fractional order sliding mode controller (nine papers), fuzzy fractional order sliding mode controller (five papers), fractional order lag-lead compensator (three papers), fractional order state feedback controller (three papers), fractional order fuzzy logic controller (three papers). Finally, several conclusions were drawn from the results that were given in this papers

Modeling and Simulation of a Control Algorithm for Home Energy Management System

Microgrids are handy units for a utility since their units such as distributed energy resources (DER) and loads can able to control the power ingestion or production. Moreover, it is used to assimilate renewable energy resources (RES) to small distribution systems. Battery energy storage systems (BESSs) are employed to recompense the sporadic output of RES. Similarly, DC microgrid for a home can be excellently controlled by an energy management system (EMS) using fuzzy logic controller (FLC) of 25-rules alone to control the power flow. The system has photovoltaic (PV), Fuel Cell (FC) and battery energy storage (BES). This study aims to introduce firefly algorithm (FA) to optimize FLC in order to increase the system energy saving efficiency and to reduce the cost.

Microgrids Connected to the Residential grid for Energy Management Utility Fuzzy Logic Controller Employed Hybrid Electric Vehicles

In our research design presents the model of managing the energy for residential purpose and for hybrid vehicle simultaneously. This system integrated with reusable energy storage system such as PV panel and battery. Our energy management system approaches the goal to satisfy power needed and to minimize the fluctuation in power microgrid. We designed the model with the fuzzy logic controller in order to optimize the microgrid power constantly. The energy sources retrieved and given to hybrid electric vehicle and residential purpose. Now a day’s most of the vehicles are hybrid mostly runs on either in fuel or by electric energy else by both energy sources. In order to achieve the maximum power throughput, we proposed the bidirectional converter is connected with PV panel and microgrid. The designed fuzzy logic controller to manage the lifetime of battery and to provide the constant discharge. The entire vehicle setup module is connected to microgrid.

A Hybrid Active Filter Using the Backstepping Controller for Harmonic Current Compensation

This document presents a new hybrid combination of filters using passive and active elements because of the generalization in the use of non-linear loads that generate harmonics directly affecting the symmetry of energy transmission systems that influence the functioning of the electricity grid and, consequently, the deterioration of power quality. In this context, active power filters represent one of the best solutions for improving power quality and compensating harmonic currents to get a symmetrical waveform. In addition, given the importance and occupation of the transmission network, it is necessary to control the stability of the system. Traditionally, passive filters were used to improve energy quality, but they have endured problems such as resonance, fixed remuneration, etc. In order to mitigate these problems, a hybrid HAPF active power filter is proposed combining a parallel active filter and a passive filter controlled by a backstepping algorithm strategy. This control strategy is compared with two other methods, namely the classical PI control, and the fuzzy logic control in order to verify the effectiveness and the level of symmetry of the backstepping controller proposed for the HAPF. The proposed backstepping controller inspires the notion of stability in Lyapunov’s sense. This work is carried out to improve the performance of the HAPF by the backstepping command. It perfectly compensates the harmonics according to standards. The results of simulations performed under the Matlab/Simulink environment show the efficiency and robustness of the proposed backstepping controller applied on HAPF, compared to other control methods. The HAPF with the backstepping controller shows a significant decrease in the THD harmonic distortion rate.

A switching-based collaborative fractional order fuzzy logic controllers for robotic manipulators

In the present work, a collaborative implementation of fractional order proportional-integral-derivative control and fractional order fuzzy logic control method is investigated for the controlled tracking performance of a manipulator plant. An integral time absolute error measured between the desired and actual output trajectories, which is obtained by the optimal parameters, drives the switching mechanism between the two controllers, fractional order proportional-integral-derivative and fractional order fuzzy logic control, in order to implement the collaboration scheme. Different possible ways of switching mechanism between fractional order proportional-integral-derivative controller and fractional order fuzzy logic control scheme are explored. The resulting controller's parameters are investigated with meta-heuristic technique namely cuckoo search algorithm. The performance of the proposed collaborative scheme is compared with existing control schemes and basic proportional-integral-derivative control method for obtaining the controlled tracking performance. To generalize the use and effectiveness of designed control scheme, the simulation studies are also investigated for a first order plant and an inverted pendulum for the trajectory tracking problem. To analyse the effects of the parameter variations, disturbance rejection, and payload variations, the performance results for the proposed scheme are also presented for robotic manipulator. The proposed controller has a potential application in robotic manipulator control for different applications.

Performance analysis of optimal hybrid novel interval type-2 fractional order fuzzy logic controllers for fractional order systems

The main objective of this paper is to propose the family of hybrid novel interval type-2 fractional order fuzzy PID (IT2FO-FPID) controllers, which incorporates fractional order integro-differentiator, taking into its consideration the advantages of the interval type-2 fuzzy controllers (IT2FLCs) and fractional order PID (FOPID) controllers. The incorporation of IT2FLCs with FOPID controllers has been investigated in terms of time response measure for unit set-point response and unit load disturbance. The resulting different hybrid IT2FO-FPID controller structures are examined on different fractional order processes followed by robustness analysis. It is observed that incorporation of fractional order integro-differential operators made the proposed control structures less sensitive to the parameters change. For this reason, fractional order integro-differential operators are adopted as design variables along with input/output scaling factors for designing of effective novel controllers. In order to do reasonable comparison, the tuning of the family of hybrid IT2FO-FPID controllers' parameters is done with newly Artificial Bee Colony-Genetic Algorithm (ABC-GA) optimization technique to develop optimal closed loop performance by considering the objective function as weighted sum of control loop error index and control output. The simulation analyses are executed for two different non-integer order plus time delay fractional order process plants (NIOPTD-I and NIOPTD-II). These processes represent a sensible estimation of most process plants and are employed to examine the comparative qualities of the presented hybrid IT2FO-FPID controllers. Further, the performance comparison and the significance of the different proposed hybrid controllers are investigated under unit set-point response, load disturbance rejection, and small control signal or actuators requirement. In order to test the efficacy of proposed controllers, the robustness analysis such as parameter variations is also investigated. Finally, the simulation investigations explicitly suggest that proposed different hybrid IT2FO-FPID control structures can be recommended in terms of better step response, better load disturbance rejection, small control signal response, and parameters variation.

Coordinated Chassis Control Based on Vehicle Lateral Acceleration Using Fuzzy Logic Control

This paper presents an advanced coordination of integrated control system which consist of three different controllers namely Electronic Stability Control (ESC), Active Front Steering (AFS), and Active Suspension (AS) using Fuzzy Logic Control (FLC) in order to improve vehicle handling, cornering stability, and rollover prevention with anti-lock brakingsystem (ABS) to avoid wheel locking during generating differential braking by ESC control. Based on a well-developed and validated fourteen degrees of freedom full vehicle model with non-linear tire characteristics, a reference yaw-roll plane vehicle model is introduced to compare and therefore control the yaw rate, side slip angle, and roll angle of the vehicle body. For rollover prevention indices, the dynamic load transfer ratio is defined to check the effectiveness of the proposed controller. The Coordination chassis control is based on the lateral acceleration value as input to FLC, and the outputs are the increment factors to the three controllers varying from zero to one. Three membership functions are chosen to represent the lateral acceleration as input to the supervisor controller, to define the control authority for each actuator. The universe of discourse for each membership function is selected according to the study of the effect of eachcontroller stand alone in vehicle performance. The numerical modelling is carried out through the MATLAB / Simulink environment which suits the control and optimization process. Different standard test maneuvers namely J-turn,fishhook, and double lane change have been carried out by considering standard test maneuvers with different driving speeds. The simulation results are compared during three cases namely, the uncontrolled system, the combined controller, and the proposed coordinated controller. The results show a substantial improvement of the vehicle stability in terms ofvehicle lateral acceleration, side slip angle, yaw rate, roll angle, and the for the developed coordinated controller compared to combined controller or the conventional system without control.

Development of hybrid artificial intelligent based handover decision algorithm

The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligent handover decision algorithm has been developed. The developed model is made up of hybrid of Artificial Neural Network (ANN) based prediction model and Fuzzy Logic. On accessing the network, the Received Signal Strength (RSS) was acquired over a period of time to form a time series data. The data was then fed to the newly proposed k-step ahead ANN-based RSS prediction system for estimation of prediction model coefficients. The synaptic weights and adaptive coefficients of the trained ANN was then used to compute the k-step ahead ANN based RSS prediction model coefficients. The predicted RSS value was later codified as Fuzzy sets and in conjunction with other measured network parameters were fed into the Fuzzy logic controller in order to finalize handover decision process. The performance of the newly developed k-step ahead ANN based RSS prediction algorithm was evaluated using simulated and real data acquired from available mobile communication networks. Results obtained in both cases shows that the proposed algorithm is capable of predicting ahead the RSS value to about ±0.0002dB. Also, the cascaded effect of the complete handover decision module was also evaluated. Results obtained show that the newly proposed hybrid approach was able to reduce ping-pong effect associated with other handover techniques.

Comments on “Design of two-layered fractional order fuzzy logic controllers applied to robotic manipulator with variable payload”

Sharma et al. have investigated the performance of two-layered fractional order fuzzy logic controller (TL-FOFLC) for a 2-link rigid planer robotic manipulator with payload. In this work, the performance of TL-FOFLC has been compared with two-layered FLC (TL-FLC), single-layered FLC (SL-FLC) and the conventional proportional-integral-derivative (PID) controllers, for trajectory tracking, model uncertainties and disturbance rejection. In this comment, it is pointed out that this work has several missing essential parameters, and therefore, it is not possible for the reader to validate all the claimed results of Sharma et al. (2016). Six numerical values, three gains for each of the used two PID controllers are found to be unreported in addition to the six gains for each of the used two SL-FLCs. Since the performances of the PIDs and the SL-FLCs are highly dependent on their tuned gains it is concluded that the reported performances of these controllers cannot be validated.

Design of two-layered fractional order fuzzy logic controllers applied to robotic manipulator with variable payload

The robotic manipulators are highly coupled and nonlinear systems wherein the time-varying parameters and uncertainties adversely affect the characteristics and response of these systems. Hence, these systems require an effective and robust controller to handle such complexities which is a difficult challenge for control engineers. This paper presents two-layered fractional order fuzzy logic controller (TL-FOFLC) scheme for a two-link planar rigid robotic manipulator with payload for trajectory tracking task. For the optimal design, the controller parameters of the proposed scheme are obtained with potential meta-heuristic technique named as cuckoo search algorithm (CSA). In order to ensure effectiveness, the performance of proposed TL-FOFLC is compared with that of its integer order design approach, i.e., two-layered FLC (TL-FLC), single-layered FLC (SL-FLC), and the conventional proportional-integral-derivative (PID) controllers. Further, the robustness testing is carried out for parameter variations and external disturbance rejection.

Fuzzy Logic Control of A Hybrid Energy Storage Module for Naval Pulsed Power Applications

There is need for an energy storage device capable of transferring high power in transient situations aboard naval vessels. Currently, batteries are used to accomplish this task, but previous research has shown that when utilized at high power rates, these devices deteriorate over time causing a loss in lifespan. It has been shown that a hybrid energy storage configuration is capable of meeting such a demand while reducing the strain placed on individual components. While designing a custom converter capable of controlling the power to and from a battery would be ideal for this application, it can be costly to develop when compared to purchasing commercially available products. Commercially available products offer limited controllability in exchange for their proven performance and lower cost point - often times only allowing a system level control input without any way to interface with low level controls that are frequently used in controller design. This paper proposes the use of fuzzy logic control in order to provide a system level control to the converters responsible for limiting power to and from the battery. A system will be described mathematically, modeled in MATLAB/Simulink, and a fuzzy logic controller will be compared with a typical controller.

A Solution Based on Bluetooth Low Energy for Smart Home Energy Management

The research and the implementation of home automation are getting more popular because the Internet of Things holds promise for making homes smarter through wireless technologies. The installation of systems based on wireless networks can play a key role also in the extension of the smart grid towards smart homes, that can be deemed as one of the most important components of smart grids. This paper proposes a fuzzy-based solution for smart energy management in a home automation wireless network. The approach, by using Bluetooth Low Energy (BLE), introduces a Fuzzy Logic Controller (FLC) in order to improve a Home Energy Management (HEM) scheme, addressing the power load of standby appliances and their loads in different hours of the day. Since the consumer is involved in the choice of switching on/off of home appliances, the approach introduced in this work proposes a fuzzy-based solution in order to manage the consumer feedbacks. Simulation results show that the proposed solution is efficient in terms of reducing peak load demand, electricity consumption charges with an increase comfort level of consumers. The performance of the proposed BLE-based wireless network scenario are validated in terms of packet delivery ratio, delay, and jitter and are compared to IEEE 802.15.4 technology.