Fuzzy Logic Control Method Research Articles

The maximum power tracking of variable step fuzzy logic control based on power prediction

The perturb and observe algorithm oscillates in the vicinity of maximum power point, and fixed step size can not be set on the premise of meeting tracking accuracy and speed. To locate the optimal operating point of photovoltaic power generation system and to solve the contradiction between tracking accuracy and speed, a variable step fuzzy logic control method based on power prediction is proposed. In other words, the variable step is introduced on the basis of power prediction method and meanwhile, the fuzzy logic control method is adopted control the variable step in real time to track the maximum power point. The comparison of simulation results of other methods and that of the method proposed in this paper shows that the method proposed cannot only track the maximum power point fastly, but also improve the dynamic performance and steady-state performance of the system. The experimental results prove the effectiveness and practicability of the algorithm.

Performance Analysis of Artificial Intelligence Based MPPT Techniques for a Solar System Under Changing Environmental Conditions

Solar energy due to this abundance and free availability, has become an important source of energy among various renewable energy sources. MPPT controller is a crucial part of a solar power-based system for extracting maximum available power at a particular irradiance and temperature condition. This paper present comparative analysis between conventional Perturb and Observe (P&O), AI based, (FLC) fuzzy logic control and ANN (Artificial Neural Network) MPPT methods for Solar PV (photovoltaic) module in changing environmental conditions. These techniques have been simulated in MATLAB/Simulink. Our analysis finds that fuzzy technique is more effective as compared to P&O and ANN methods.

Voltage Regulation in BDC Based on Fuzzy Logic Controller using Solar Power Generation

The PhotoVoltaic (PV) based grid system coupled with Bidirectional DC-DC Converter (BDC) utilize Fuzzy Logic Controller (FLC) for increasing voltage gain and reduce the settling time of DC link voltage than conventional is presented. BDC satisfied the load requirements, and control the power flow from different sources such as PV, grid, and battery. However, problems in conventional system are high Total Harmonic Distortion (THD), DC link voltage gain and settling time of capacitor voltage. The generated power is used for improving the power quality at the output of the inverter using Sliding Mode Controller (SMC). The converter and inverter operate has bidirectional performance and utilize the hybrid power generation as mentioned. The battery can act as a load based on operating modes of BDC and power generation. It provides a comparative analysis of Proportional Integral (PI) and FLC method that is effectively performs harmonic reduction in BDC.

Implementation of a Fuzzy Logic Control Strategy on a Harvester’s Controller Based on MATLAB Environment

Due to the nonlinear process of grain harvesting, there is no precise mathematical model to describe the behavior of the cleaning system of a harvester. Both the classical control and modern control methods cannot fulfil the requirements. Owing to this, the intelligent control algorithm was proposed, and the fuzzy logic control (FLC) method is a type of this. At present, most FLC algorithms are proposed in a MATLAB environment. However, the control problems in reality are controlled by microcomputer controllers with different chips. The control language of the microcomputer controller is usually written in C language. It is impossible to directly migrate the algorithm between these two different languages. Therefore, it is an important issue to transplant the FLC algorithm procedure written by MATLAB to the microcomputer controller. To realize the above target, we have built a complete set of control systems for our harvester’s cleaning system based on an upper computer and an STM32 core-chip controller. By means of combining FLC theory and expert knowledge, we adopted an improved FLC algorithm for the cleaning system, which is mounted in our self-designed combine harvester. Through this scheme, we have realized the objective of migrating the FLC algorithm from a MATLAB environment to the controller. The results of the experiment show that our method is reliable.

A Vision-Based Robotic Laser Welding System for Insulated Mugs with Fuzzy Seam Tracking Control

The symmetrical insulated mug is composed of two layers. The two ends of the two layers form the mouth and bottom seams of the insulated mug. The weld quality of the two seams is very important to keep the vacuum degree of the air between the two layers, which is vital for the heat-insulating property of the mug. Due to the narrow seam, laser welding is used. Since laser welding has high demand on the relative position of the seam and the laser torch, a vision-based seam tracking system is designed. Before welding is started, the vision sensor scans the seam and feature sample points are collected. A reconstruction algorithm is proposed to form the image containing the seam. Then, a least square fitting (LSF) method combined with random sample consensus (RANSAC) method is proposed to detect the smooth seam from the sample points. In the welding process, a seam tracking system with fuzzy logic control method is presented to keep the torch precisely on the seam. Finally, full experiments are conducted in the welding factory of the insulated mugs to verify the effectiveness of the proposed system and method.

Research on Implementation Method of Fuzzy Logic Control Based on MCU

Fuzzy control belongs to the category of intelligent control, which has outstanding control advantages for more complex systems. Firstly, based on Matlab sltank fuzzy control system, a data extraction model has been established in this paper. By running the model, a number table can be obtained. Secondly, the number table is embedded into the two-dimensional lookup table module, and the C code is generated by real time workshop (RTW) function. Finally, the interpolation principle analysis is carried out, and the interpolation algorithm of two-dimensional lookup table is embedded into the MCU for testing. The real-time test results show that the interpolation result of the MCU is consistent with the off-line simulation result, and the real-time control of the MCU can be guaranteed.

Speed Control of a Mobile Robot Using Fuzzy Logic Controller

Applying a speed control system to a mobile robot needs to be done to make the robot move stably in an uneven environment with the variation of load. This study purpose is to designed a speed control system for a mobile robot to follow setpoints with a fast response to reach a predetermined speed. The speed controller used the fuzzy logic control method. The input is a speed error and the change in speed error is known from an encoder sensor and the output of the system is pulse width modulation (PWM) in the range of 8-bit converted to fuzzy forms. The rule formation and rule analysis with its implication function use the MIN function and defuzzification use the weighted average method. By implementing this fuzzy control the results of the robot move at the speed corresponding to the setpoint both on flat or uneven terrain, with load or without load. In experiments carried out on robots with a setpoint of 9,000 rpm from rest, the response time (in rise-time) is less than 47ms and steady-state error close to zero.

Speed and steering control system for self-driving car prototype

A self-driving car is a vehicle that can run autonomously using a control. There are two systems that are controlled in this research. The first-speed control functions to regulate the speed and movement of the self-driving car prototype by adjusting the PWM value on the DC motor and second, the steering control uses the Ackerman steering system with a servo motor as an actuator. Both are arranged using fuzzy logic control methods that adapt from the habits in driving a car. the goal self-driving car prototype can walk to follow the track, maintain the robot car in the middle of the lane, can adjust the speed when turning and can stop when there are obstacles or traffic lights. The results of the control design testing in this study are, the average error value in the simulation is 0.771008 for the servo angle and 0.392072 for the speed. The error value in the programming algorithm is 0.149712 for servo angles and 0.198168 for PWM DC motors. Robot cars in accordance with the logic of the fuzzy rules made. Self-driving car prototypes can run turns and follow trajectories with a success rate of 93.33%. The distance between the robot car and the track is 1.83 cm inside the track and 0.03 cm outside the track. The self-driving car prototype can adjust its speed and can stop when there are obstacles with an average distance of 29.89 cm.

PENGAMBILAN KEPUTUSAN UNTUK KUALITAS AIR PADA TAMBAK UDANG MENGGUNAKAN FUZZY LOGIC CONTROL

Indra Dwisaputra, Boy Rolastin, Irwan, Angga Satera, in this paper explain that one commodity that can be used as a superior post-mining area such as Bangka Belitung is shrimp ponds. Touch of technology is needed because of high rainfall in this area so that changes in temperature, humidity, salinity and pH are more dynamic. Then we need a control to determine the water quality in shrimp ponds to avoid crop failure. This research was conducted by means of simulations to make it easier to analyze the results before applying. Fuzzy logic control (FLC) method is used for the water quality decision making system. By utilizing 3 input variables such as temperature, salinity and pH of water and output is an index of water quality from a scale of 0-100. The Mamdani method is applied to FLC with 27 fuzzy rules. The simulation results show that a change in one of the input variables can cause water quality to drop to 70. When there are changes in the two input variables, it can reduce water quality by 25.1. FLC can work well in decision making for water quality in shrimp culture.

A Fuzzy Logic Control Method for MPPT to Improve Solar System Efficiency

Solar energy is act clean as well as renewable source of energy. Hence the use of PV systems has used in many applications. Big-spread use has resulted in decreased solar panel production expenses. But the low effectiveness of a solar panel owing to variables such as solar insulation, clouds and shading impact continues one of the greatest issues. Due to variable concentrations of isolation, the panel output remains variable during cloudy weather. To increase the solar panel's efficiency, the maximum algorithm for power point tracking needed as it was discovered only a small percentage of the energy incident is transformed into electricity. DC-DC Converter (Boost Converter) design helps to increase the panel output, thereby improving effectiveness and output voltage using reasonable control technique. This article discusses the development of the fluctuating MPPT logic control to track the maximum output point and also to compensate for fluctuating power during cloudy weather. The suggested primary circuit block diagram. The simulation study is carried out and the proposed circuit is implemented by hardware. MATLAB Simulink is used for simulating the circuit.

Optimizing the traffic control system of Sultan Agung street Yogyakarta using fuzzy logic controller

The traffic control system is a system that functions to regulate and control traffic flow at crossroads, pedestrian crossings (zebra crossing) and other traffic flows. A good traffic control system is a system that is able to adjust the duration of the traffic lights with the density of traffic flow automatically. In big cities like Yogyakarta, traffic congestion is one of the most common problems. It is caused by the traffic lights that use a conventional regulation system. This study aims to discuss the establishment of a traffic light regulation system on Sultan Agung street and surrounding areas where there are nearby traffic lights and are interrelated with one another. Determining the duration of the green light is calculated using the fuzzy logic controller method with 3 input variables namely the number of cars, the number of motorcycles, and the length of the queue. The results of the traffic light regulation system using fuzzy logic controllers are more effective than conventional system settings. It is because the system is able to adjust the duration of the traffic lights with traffic conditions at that time, so the duration of traffic light is varied for each traffic conditions.

DESIGN OF FUZZY LOGIC CONTROLLER FOR CHARGING SYSTEM WITH TEMPERATURE CONDITION SYSTEM IN EXCAVATOR

Batteries have an important role in the development of energy needs. A good battery performance, will support the device it supports. The amount of battery that can be stored is limited, the battery will experience a charge and discharge cycle. One aspect of battery management is how the temperature in the battery is always controlled. This will place the battery always in the ideal temperature so that it can work optimally. The stages in software design consist of designing a fuzzy logic controller that uses Arduino software. To display the results of reading the system, the Serial Monitor is used in the Arduino software. From the data, the response from the control used will be observed. The system is controlled using the Fuzzy Logic Controller method. In this experiment, the input used is the LM35 temperature sensor. This value will affect the speed of the DC motor which in this experiment is regulated through the determination of the PWM value. There are 3 PWM values, namely 100 for slow motor speed, 200 for medium motor speed and 255 for fast motor speed.

A robust SMES controller strategy for mitigating power and voltage fluctuations of grid-connected hybrid PV–wind generation systems

Recently, hybrid generation systems (HGSs) are considered to be the optimal solution for supplying remote areas with the required electrical power. HGSs contain two or more renewable energy sources (RESs), such as wind power generators (WPGs), photovoltaic (PV) generation systems and the energy of the tides. Due to the intermittent nature of RESs, power and voltage fluctuations have appeared accordingly at the connection point with the utility power grid. Hence, energy storage technologies can play an effective role in mitigating power and voltage fluctuations caused by hybrid PV–wind generation systems. This paper proposes a robust control strategy for superconducting magnetic energy storage (SMES) combined with HGS. Fuzzy logic control (FLC) is used to control the chopper circuit of SMES in order to charge/discharge the active power from/to HGS. The proposed FLC strategy considers the fluctuations due to PV and wind power generation systems and the random load power variation. SMES with the proposed FLC method can effectively mitigate the fluctuation of active and reactive powers transfer between HGS and grid, and it can also regulate the voltage profile of the common connection point of SMES with HGS. Moreover, the system line power loss is clearly reduced by using the SMES system. The complete power system including loads is built in MATLAB/Simulink platform. The simulation results indicate that the proposed FLC–SMES can smooth both power and voltage fluctuations due to uncertain climate changes of PV and WPG systems.

Minimization of Adverse Effects of Time Delay on Power Quality Enhancement in Hybrid Grid

One of the most challenging issues of the modern power grid is the communication delay, which has adverse effects on the power quality. This paper proposes two methods, such as the fuzzy logic controller (FLC) based approach and the modified predictor method, to minimize the negative effects of time delay on the power quality enhancement in a hybrid power grid consisting of synchronous and wind generators. The thyristor switched capacitor has been used as a power quality enhancement means for the grid. Time delays ranging from 0–700 ms have been considered for the analysis. Both balanced and unbalanced temporary and permanent faults at different locations in the power network have been considered. Two indices, namely the voltage index and the total harmonic distortion have been used to evaluate the power quality of the system. The performance of the FLC-based method has been compared with that of the modified predictor method. Simulations have been performed by using the MATLAB/Simulink software. Simulation results show that both the proposed methods can minimize the negative effects of time delays on the power quality enhancement of the system. However, the FLC method performs better than the modified predictor method.

Improved MPPT method to increase accuracy and speed in photovoltaic systems under variable atmospheric conditions

The changes in temperature and radiation cause visible fluctuations in the output power produced by the photovoltaic (PV) panels. It is essential to keep the output voltage of the PV panel at the maximum power point (MPP) under varying temperature and radiation conditions. In this study, a maximum power point tracking (MPPT) method has been developed which is based on mainly two parts: the first part is adapting calculation block for the reference voltage point of MPPT and the second one is Fuzzy Logic Controller (FLC) block to adjust the duty cycle of PWM applied switch (Mosfet) of the DC-DC converter. In order to evaluate the robustness of the proposed method, Matlab/Simulink program has been used to compare with the traditional methods which are Perturb & Observe (P&O), Incremental Conductance (Inc. Cond.) and FLC methods under variable atmospheric conditions. When the test results are observed, it is clearly obtained that the proposed MPPT method provides an increase in the tracking capability of MPP and at the same time reduced steady state oscillations. The accuracy of the proposed method is between 99.5% and 99.9%. In addition, the time to capture MPP is 0.021 sec. It is about four times faster than P&O and five times faster than for Inc. Cond. and, furthermore, the proposed method has been compared with the conventional FLC method and it has been observed that the proposed method is faster about 28% and also its efficiency is about 1% better than FLC method.

Leader-Follower Formation System of Multi-Mobile Robots for Gas Source Searching

Currently, robots are used to help dangerous human activities, including monitoring and searching for sources of toxic and flammable gases. One of the characteristics of a gas leak is that the direction of the gas changes easily due to wind. Therefore, when using a mobile robot, it will easily lose the direction of the gas plume which results in the longer time needed to find the gas source. In this study, the method of searching for gas source has been developed with the leader-follower formation system. This formation aims to make the multi-mobile robots not easily lose the direction of the gas plume. A simple Particle Swarm Optimization (PSO) algorithm is used to configure V-shaped formation and keep the distance among the robots. The XBee module is used to transmit the data and measure the distance of each robot using the Received Signal Strength Indicator (RSSI) value. The Fuzzy Logic Control (FLC) method is applied to mobile robot navigation to track the gas plume. The PSO algorithm and the FLC method are implemented in the Arduino Mega 2560 microcontroller. The experimental results show that the mobile robot team can maintain a distance of 1 to 2 meters between each robot.

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.

Real-time control and application with self-tuning PID-type fuzzy adaptive controller of an inverted pendulum

PurposeThis paper aims to keep the pendulum on the linear moving car vertically balanced and to bring the car to the equilibrium position with the designed controllers.Design/methodology/approachAs inverted pendulum systems are structurally unstable and nonlinear dynamic systems, they are important mechanisms used in engineering and technological developments to apply control techniques on these systems and to develop control algorithms, thus ensuring that the controllers designed for real-time balancing of these systems have certain performance criteria and the selection of each controller method according to performance criteria in the presence of destructive effects is very helpful in getting information about applying the methods to other systems.FindingsAs a result, the designed controllers are implemented on a real-time and real system, and the performance results of the system are obtained graphically, compared and analyzed.Originality/valueIn this study, motion equations of a linear inverted pendulum system are obtained, and classical and artificial intelligence adaptive control algorithms are designed and implemented for real-time control. Classic proportional-integral-derivative (PID) controller, fuzzy logic controller and PID-type Fuzzy adaptive controller methods are used to control the system. Self-tuning PID-type fuzzy adaptive controller was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of real-time with self-tuning PID-type fuzzy adaptive controller.

An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

A Fuzzy Logic-based MPPT Technique for PMSG Wind Generation System

Permanent magnet synchronous generator (PMSG)-based wind turbine is considered as one of the promising technologies for generating electric power from wind energy. This paper presents an artificial intelligent technique based on fuzzy logic controller (FLC) to enhance the maximum power point tracking (MPPT) of PMSG-based wind turbine. The FLC method is compared with the conventional methods of MPPT to clarify the superior features of the proposed technique. The comparison between the MPPT methods have been accomplished based on the speed response and new evaluation where the overall system efficiency is considered. A complete model of PMSG with a back to back (BTB) converter along with the control system considering different MPPT techniques is conducted using MATLAB/Simulink O platform. To reveal the robustness and the feasibility of control system, the system is examined under different wind speed profiles. On the basis of the results, good tracking with high accuracy and lower oscillation rate are obtained after using FLC. Moreover, the overall system efficiency is enhanced compared with other MPPT schemes.