Output Of Fuzzy Logic Controller Research Articles

Temperature and Humidity Control System for 20 kV of Cubicle with Multiple Input Multiple Output Fuzzy Logic Controller

Cubicle 20 kV is a crucial electrical equipment in the 20 kV power distribution system. Often, cubicle issues arise due to excessively low or high temperatures and humidity, which can lead to the presence of water spots and corrosion on the components inside the 20 kV cubicle. One of the efforts to maintain the reliability of this 20 kV cubicle is to ensure that the temperature and humidity inside the cubicle remain within their operational limits. To achieve this, a system is needed to control the temperature and humidity in the 20 kV cubicle. The system can monitor and control the temperature and humidity inside the cubicle by adjusting the activation angle of the exhaust fan and heater. Control is achieved using a multiple-input, multiple-output fuzzy logic controller. The main components of this system are the STM32 microcontroller, DHT22 sensor, and ESP8266 module for monitoring temperature and humidity via a website. System has successfully controlled temperature and humidity d with a set point value of 35 °C and humidity of 60% RH. This implementation of fuzzy multiple input multiple outputs has performed well and resulted in only a small error of 0.55% for temperature and 1.05% for humidity.With the presence of this device, the temperature and humidity in the cubicle 20 kV can be controlled, and it enables the PLN operator to easily monitor and maintain the cubicle 20 kV.

Electric Bike Range Estimation using Fuzzy Logic Controller

Electric Vehicles (EV) are the prompt solution to significantly lowering the use of fossil fuels and CO2 emissions from the transport industry. There is a continuing growth in the number of EVs in use, but their huge acceptance by customers is associated to the quality they can provide. Nowadays, different types of electric vehicles are moving toward green awareness, and one among them is the electric motorcycle which is a considerable vehicle in India. Though there are many benefits of driving electric motorcycles, because of the limited driving range and inadequate charging stations, it is still not generally accepted in the industry. Range anxiety is the major market concern that is solved by the implementation of an additional range estimating technique that can ease the "range anxiety" caused by the restricted range of EVs. Therefore, this paper proposes a fuzzy logic controller model for the estimation of the EV range based on the battery's state of charge and the load's power usage. In this work, the load power consumption of the vehicle and the status of the battery charge are selected as inputs and the EV range is selected as a Fuzzy Logic Controller output. This model is implemented in the Matlab/Simulink environment.

Performance Analysis of a High Gain LUO Converter-Based Hybrid PV-Wind System

This project is aimed at the implementation of a fuzzy logic algorithm based maximum power point tracking in transformer less grid connected PV system along with reactive power compensation. A single diode model is used for PV array and its simulation is performed using MATLAB. In fuzzy logic controller, voltage and current are taken as inputs and the effective value of A.C current corresponding to the maximum power point is obtained as output. Thus, in addition to supplying voltage by the inverter without transformer for compensating the reactive power not exceeding its power rating. This results in utilization of PV system at night and at periods of low irradiation. Rules relating the input and output of fuzzy logic controller are written and simulation is performed. A LUO Converter is used for maintaining DC input to the inverter at various conditions of irradiation and temperature. Gating pulses to the inverter are generated by proportional-integral controller. Hardware model of a10W solar panel is developed and results are obtained with fuzzy logic controller for different irradiation and temperature conditions. Results show the effectiveness of the proposed method in utilizing the PV system. This project is implemented using DSPIC30F2010 controller.

Implementation of Fuzzy Logic Controller for Wall Following and Obstacle Avoiding Robot

This paper presents the development of wall following and obstacle avoiding robot using a Fuzzy Logic Controller. The ultrasonic sensors are employed to measure the distances between robot and the wall, and between the robot and the obstacle. A low cost Raspberry Pi camera is employed to measure the left/right distance between the robot and the obstacle. The Fuzzy Logic Controller is employed to steer the mobile robot to follow the wall and avoid the obstacle according to the multi sensor inputs. The outputs of Fuzzy Logic Controller are the speeds of left motor and right motor. The experimental results show that the developed mobile robot could be controlled properly to follow the different wall positions and avoid the different obstacle positions with the high successful rate of 83.33%.

Numerical Investigation on Fuzzy Logic Control Energy Management Strategy of Parallel Hybrid Electric Vehicle

In order to further reduce fuel consumption, a double input and single output fuzzy logic controller has been established for the energy management of a parallel hybrid electric vehicle (HEV). In this paper, the fuel economy and battery state of charge(SOC) of vehicles has been numerically investigated under both NEDC and WLTC driving cycles. Results showed that compared with the logic threshold control strategy, the fuel consumption under the NEDC and WLTC driving cycles are reduced by 13.3% and 4.5%, respectively, when the fuzzy logic control strategy was adopted. In addition, compared with the logic threshold control strategy, the fluctuation of SOC variation of the fuzzy logic control strategy is much smaller under both driving cycle, which has a positive effect on increasing the effectiveness of the battery discharge, maintaining stability the battery operating, and extending the battery life.

Performance Comparison of MANET, VANET and FANET

A wireless ad hoc network is a decentralized kind of wireless network. It is a kind of temporary Computer-to-Computer connection. It is a spontaneous network which includes mobile ad-hoc network (MANET), vehicular ad-hoc network (VANET) and Flying ad-hoc network (FANET). Mobile Ad Hoc Network (MANET) is a temporary network that can be dynamically formed to exchange information by wireless nodes or routers which may be mobile. A VANET is a sub form of MANET. It is an technology that uses vehicles as nodes in a network to make a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. In this research paper Fuzzy based control approaches in wireless network detects & avoids congestion by developing the ad-hoc fuzzy rules as well as membership functions.In this concept, two parameters have been used as: a) Channel load b) The size of queue within intermediate nodes. These parameters constitute the input to Fuzzy logic controller. The output of Fuzzy logic control (sending rate) derives from the conjunction with Fuzzy Rules Base. The parameter used input channel load, queue length which are produce the sending rate output in fuzzy logic. This fuzzy value has been used to compare the MANET, FANET and VANET in terms of the parameters Throughput, packet loss ratio, end to end delay. The simulation results reveal that usage of Qual Net 6.1 simulator has reduced packet-loss in MANET with comparing of VANET and FANET.

Line Follower Robot Optimization based Fuzzy Logic Controller Using Membership Function Tuning

The study was aimed to measure the performance of Fuzzy Logic Controller (FLC) on Line Follower Robot (LFR). FLC output is a deviation value of Pulse Width Modulation (PWM) to determine the rotational speed of the left and the right wheel. As input variables are current and previous line sensors. Tuning was applied to input and output variables in each membership function (MF) to conduct the best performance. This study used triangular membership function that consists of three MF. Mamdani Fuzzy Inference System (FIS) is used using nine rules. The result obtains that after MF tuning, the performance of the LFR settling time is 0.63s faster compare to that without tuning.

Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter

In this study, a maximum power point tracking DC–DC quadratic boost converter for high conversion ratio required applications is proposed. The proposed system consists of a quadratic boost converter with high step-up ratio and fuzzy logic based maximum power point tracking controller. The fuzzy logic based maximum power point tracking algorithm is used to generate the converter reference signal, and the change in PV power and the change in PV voltage are selected as fuzzy variables. Determined membership functions and fuzzy rules which are design to track the maximum power point of the PV system generates the output signal of the fuzzy logic controller's output. It is seen from MATLAB/Simulink simulation and experimental results that the quadratic boost converter provides high step-up function with robustness and stability. In addition, this process is achieved with low duty cycle ratio when compared to the traditional boost converter. Furthermore, simulation and experimental results have validated that the proposed system has fast response, and it is suitable for rapidly changing atmospheric conditions. The steady state maximum power point tracking efficiency of the proposed system is obtained as 99.10%. Besides, the output power oscillation of the converter, which is a major problem of the maximum power point trackers, is also reduced.

Effective Demand Side Scheme for PHEVs Operation Considering Voltage Stability of Power Distribution Systems

Abstract This paper presents a new technique for a controlled charging/discharging of the PHEV’s battery considering the network status on a real-time basis. A control strategy is developed to secure the operation of power system irrespective of the number of vehicles and their connection place. The proposed control strategy utilizes an improved normalized steady-state voltage stability index that is easy to compute in the distribution management systems. A fuzzy logic controller (FLC) is used for evaluating the battery level of charging/discharging of individual PHEV connected to the distribution network. The controller is a part of the smart charger which uses the battery state of charge and the stability index as input variables. Based on the FLC output, the interface converter of a PHEV decides the desired charging/discharging levels of the battery. The developed controller ensures the secure operation of the distribution system during charging since only empty batteries will have a high priority to charge. A time domain, as well as 24-hour time-series simulations, are used to test the proposed control method. The results show that the developed control method guarantees secure operation, whatever the number and location of PHEVs connected to the studied system.

FUZZY LOGIC CONTROL FOR ANKLE FOOT ORTHOSES EQUIPPED WITH MAGNETORHEOLOGICAL BRAKE

This study focused on the development of passive control ankle foot orthosis (PICAFO) for a specific purpose such as preventing foot drop in the post-stroke patient. The PICAFO utilized the magnetorheological (MR) brake as the actuator in which the braking torque was controlled by regulating direct current (DC) from current driver. The Fuzzy Logic Controller (FLC) was employed to control output voltage for current driver based on the inputs, i.e. Electromyography (EMG) bio signal and ankle position. Walking experiment to test the controller was carried out on a single subject where the input and output FLC was monitored and logged. The results showed that the output voltage of the FLC was 94.41% of the maximum output (high) on forward ankle position during swing phase and gradually increase from 9.667% to 77.34% of maximum output during stance phase. The FLC successfully controlled the output voltage according to the required needs. According to the experimental results, the FLC strategy was applicable for PICAFO realizing it contributes to prevention of foot drop.

Design of Path Planning and Obstacle Avoidance for a Wheeled Mobile Robot

This paper proposed a path tracking method for wheeled mobile robot (WMR). This method includes path planning and controller design. In path planning, using B-spline instead of A* algorithm to create smooth and obstacle-avoidance path, so that the possibility of collision can be reduced statistically. In controller design, genetic algorithm (GA) and fuzzy logic control (FLC) are combined together for the velocity control for WMR, which is supposed to move in a certain environment. The suitable membership function of FLC according to the feature of environment, such as distance and angle of WMR, is based on the B-spline path, and the input and output of FLC can be adjusted by GA. Therefore, the WMR can move along the planned path steady, which means our method has good performance.

Neuro-fuzzy-based hybrid controller for stable temperature of liquid in heat exchanger

The liquid heating system is a temperature sustaining system which used to preserve the liquid temperature at a constant value at different temperature ranges. In this paper, neuro-fuzzy controller (NFC)-based control technique is proposed for achieving the desired liquid temperature. NFC is the combination of neural network (NN)-based controller and fuzzy logic controller (FLC). Initially, the error temperature and change of error temperature of a liquid heating system are determined. Then, the temperature variation is applied separately to FLC and NN-based controller. From the output of FLC and NN-based controller, the mean liquid temperature ratio of the condenser is calculated. Then based on the liquid temperature ratio, the automatic control valve (ACV) is adjusted till the desired temperature is reached. The proposed system is modelled in simulation platform and the output performances are evaluated. Then, the temperature stabilising performance of proposed controller is compared with FLC and ANN.

A Fuzzy Logic Controller with Tuning Output Scaling Factor for Induction Motor Control Taking Core Loss into Account

This paper presents a design of a fuzzy logic controller (FLC) with tuning output scaling factor for speed control of indirect field oriented induction motor (IM) taking core loss into account. The variation of output scaling factor of FLC depends on the normalized output of FLC. Firstly the speed control of IM taking core loss into account is presented by using FLC with fixed scaling factors (FLC-FSF). Secondly the speed controller based on suggested FLC with tuning output scaling factor (FLC-TOSF) is proposed. The performance of the proposed FLC-TOSF for speed control of IM are investigated and compared to those obtained using FLC-SFS at different operating conditions and variation of parameters. A comparison of simulation results shows that the convergence of actual speed to reference speed is faster by using the proposed FLC-TOSF.

Design and Simulation of Dynamic Voltage Restorer based on Fuzzy Controller Optimized by ANFIS

The fuzzy logic controller (FLC) appears to be the unique solution when the process is too complex for analysis by conventional techniques or when the available information data are interpreted qualitatively, inexactly or with uncertainty. In literature, the proposed FLC in general consists of two inputs (error and derivative of error) and one output. The number of membership functions is chosen in most cases to be five or seven regardless of the approach used for the design. In this paper, we propose Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to optimize the two inputs one output FLC with seven membership functions to one input one output FLC with three membership functions without compromising accuracy. The study is applied to control a Dynamic Voltage Restorer (DVR) in voltage sag/swell mitigation. The results of simulation using MATLAB/SIMULINK show that the performance of the optimal FLC generated by ANFIS is comparable with the initial given FLC. DOI: http://dx.doi.org/10.11591/ijpeds.v4i2.5685

Asymmetric SVM Technique for Minimizing Switching Loss of Inverter

In power electronics, the pulse width modulation (PWM) inverter is one of the families of electrical circuits that converts the electrical supply from one level to another using semiconductor-based electronic switches. The important characteristic of these types of circuits is that the switches are operated only in one of two states: ON or OFF. The continuous conduction operations of these transitions cause switching power losses, which lead to power loss in PWM inverter. The purpose of the switch is to assist the process of transferring power from source to load. So, these power losses affect the performance and efficiency of the load. To overcome these problems, in this paper, asymmetric space vector pulse width modulation (ASVPWM) technique is proposed. In the proposed ASVM, the additional voltage sectors’ switching states are determined by state transition matrix. Subsequently, using the proposed technique, the speed of the induction motor (IM) is controlled. The error speed and change of error speed of an IM are calculated using a fuzzy logic controller (FLC). The output of FLC is applied to the ASVPWM inverter and the stability of the IM is improved. Then, the switching loss and harmonics effect of the proposed ASVPWM inverter are analyzed.

Power Factor Correction Using Fuzzy Logic Control

Harmonic pollution and low power factor in power systems caused by power converters have been a great concern. To overcome these problems several converter topologies and control schemes have been proposed in recent years. This paper is proposed to study the control techniques for such converters to improve the Power Factor (PF) and reduce total harmonic distortion (THD) in the input current with output voltage regulation. A new fuzzy logic control strategy for boost PFC is presented in this paper. Here the controlling action was established through a fuzzy Logic controller. The proposed Fuzzy Logic control system is a two input one output Fuzzy Logic Controller. The proposed PFC control is derived based on Boost converter operates at continuous conduction mode and the switching frequency is much higher than the line frequency so that input voltage can be assumed as a constant during one switching cycle.

Power quality problem mitigation by unified power quality conditioner: an adaptive hysteresis control technique

An adaptive hysteresis current and voltage control technique for controlling Unified Power Quality Conditioner (UPQC) is proposed in this paper. The novelty of the proposed method is the series and shunt active power filters (APF) present in the UPQC has been controlled by the separate adaptive controllers for reducing the voltage and current perturbations. Here, the adaptive control technique utilises Artificial Neural Network (ANN) and Fuzzy Logic Controller (FLC) for generating the control pulses of the series and shunt APFs. In the adaptive control technique, ANN builds a target hysteresis control data based on the inputs such as voltage and current. By using the hysteresis control data, the control parameters are generated by the FLC. According to the output of FLC, PWM pulses are generated for controlling the APFs. The effectiveness of the proposed adaptive hysteresis control technique is identified by using the comparison analysis with the existing techniques.

Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system

This paper presents the development of an optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. The FLC inputs are batteries state of charge (SOC) and net power flow, FLC outputs are the power rate of batteries, photovoltaic and diesel generator. Data for weekly solar irradiation, ambient temperature and load profile are used to tune the proposed controller by using cuckoo search algorithm. The optimized FLC is able to minimize loss of power supply probability (LPSP), excess energy (EE) and levelized energy cost (LEC). Moreover, the results of CS optimization are better than of particle swarm optimization PSO for fuzzy system controller.

Fuzzy logic control for active bus suspension system

In this study an active controller is presented for vibration suppression of a full-bus suspension model that use air spring. Since the air spring on the full-bus model may face different working conditions, auxiliary chambers have been designed. The vibrations, caused by the irregularities of the road surfaces, are tried to be suppressed via a multi input-single output fuzzy logic controller. The effect of changes in the number of auxiliary chambers on the vehicle vibrations is also investigated. The numerical results demonstrate that the presented fuzzy logic controller improves both ride comfort and road holding.

Optimization of Semi-active Suspension System Using Particle Swarm Optimization Algorithm

The paper involves modeling of quarter car models of passive and various semi-active suspension systems. Semi-active suspension is better than the passive and active suspensions based on optimum performance within the constraints of weight and operational cost. Fuzzy logic controllers are incorporated in the design scheme of semi-active models. Fuzzy logic based systems can handle non-linearities through heuristic rules. Particle swarm optimization technique is applied in order to determine the optimized scaling factors keeping the normalized ranges of inputs and output of fuzzy logic controller. The performance of resulting optimized system is compared with different systems based on various control algorithms (including passive system). The models are compared for attributes of road handling and ride comfort. The results clearly manifest supremacy of fuzzy logic based particle swarm optimized semi-active suspension system in relation to all other systems. The research presents an innovative approach of utilizing relative displacement and relative velocity as the input parameters resulting in no overshoot of the suspension displacement.