A fuzzy inference engine in nonlinear analog mode and its application to a fuzzy logic control

1993 ◽  
Vol 4 (3) ◽  
pp. 496-522 ◽  
Author(s):  
T. Yamakawa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Inference ◽  
Inference Engine ◽  
Fuzzy Inference Engine ◽  
Logic Control ◽  
Nonlinear Analog

scholarly journals Through-Wall Image Enhancement Using Fuzzy and QR Decomposition

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Muhammad Mohsin Riaz ◽  
Abdul Ghafoor
Keyword(s):  
Fuzzy Logic ◽  
Singular Value Decomposition ◽  
Image Enhancement ◽  
Visual Inspection ◽  
Fuzzy Inference ◽  
Qr Decomposition ◽  
Inference Engine ◽  
Singular Value ◽  
Fuzzy Inference Engine ◽  
Value Decomposition

QR decomposition and fuzzy logic based scheme is proposed for through-wall image enhancement. QR decomposition is less complex compared to singular value decomposition. Fuzzy inference engine assigns weights to different overlapping subspaces. Quantitative measures and visual inspection are used to analyze existing and proposed techniques.

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

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 132 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
DoHyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Environmental Temperature ◽  
Fuzzy Inference ◽  
Control Method ◽  
Learning Algorithm ◽  
Membership Functions ◽  
Inference Method ◽  
Logic Control

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.

Fuzzy Inference Methods Employing T-norm with Threshold and Their Implementation

2003 ◽  
Vol 7 (3) ◽  
pp. 362-369 ◽  
Author(s):  
Bui Cong Cuong ◽  
◽  
Nguyen Hoang Phuong ◽  
Ho Khanh Le ◽  
Bui Truong Son ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Inference Engine ◽  
Fuzzy Inference Engine ◽  
Reasoning Systems ◽  
Different Types ◽  
The Many ◽  
Inference Methods

The fuzzy inference engine is an important part of reasoning systems. Among the many different types of inference, MATLAB is a powerful tool including many useful toolboxes, one of which is the Fuzzy Logic Toolbox. To improve toolbox capacity, we programmed and installed several new inference methods.

Heading Control of a RC Helicopter by Fuzzy Logic

2013 ◽  
Vol 278-280 ◽  
pp. 1466-1472
Author(s):  
Anan Suebsomran
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Fuzzy System ◽  
Fuzzy Logic Control ◽  
Fuzzy Inference ◽  
Feedback Signal ◽  
Inference System ◽  
Control Approach ◽  
Logic Control ◽  
Heading Control

This research aims to control the heading of a small RC helicopter based on fuzzy logic control approach. Control of such a vehicle is normally nonlinear system. To be linear system, the mathematical model of this system would be archived and its parameters. From such difficulty of obtaining mathematical model and parameters, thus in this propose we develop the heading control of a small RC helicopter by using fuzzy logic control, which is overcome the unmodeling of such vehicle, for objective of developed control system. The performance of system is specified to control in time domain design specification. The rule based construction for designing the fuzzy system is related to input and output membership function for inference fuzzy system. Second order response time is specified, and error e and delta error (e) are conditionally assigned for constructing the rule of fuzzy inference system using Mamdani reasoning scheme. Defuzzification method applies the center of gravity (COG) method to computation the output to compensate the error and delta error signal. Finally the result of experiment reveals that the tracking of control of desire heading command and feedback signal is archived via the desired control performance in heading control of a small RC helicopter system.

scholarly journals Application of Fuzzy Theory and Optimum Computing to the Obstacle Avoidance Control of Unmanned Underwater Vehicles

2020 ◽  
Vol 10 (17) ◽  
pp. 6105 ◽  
Author(s):  
Shihming Chen ◽  
Tsungyin Lin ◽  
Kaiyi Jheng ◽  
Chengmao Wu
Keyword(s):  
Fuzzy Logic ◽  
Obstacle Avoidance ◽  
Fuzzy Logic Control ◽  
Fuzzy Inference ◽  
Inertial Sensors ◽  
Fuzzy Theory ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Unmanned Underwater Vehicles ◽  
Logic Control

Autonomous underwater vehicles and remotely operated vehicles (ROVs) are unmanned underwater vehicles widely used in marine environments. Establishing an efficient obstacle avoidance approach in underwater environments remains a challenge for these vehicles. Most studies have relied on simulated results; few have been conducted with vehicles in a real environment. This study used an ROV equipped with a scanning sonar as an experimental platform and applied fuzzy logic control to solve nonlinear and uncertain problems, which are difficult to address using conventional control theory. Using data from the depth and inertial sensors, fuzzy logic control can output defuzzification command values that are passed through a fuzzy inference engine to control ROV motion. Fuzzy logic control was used to evaluate depth and heading degrees in navigation experiments. In heading navigation, scanning sonar was used to detect obstacles in the scanning range. An optimum navigation strategy was also developed to calculate appropriate headings to safely and stably navigate during a mission to attain a predetermined destination. The results indicated that the ROV with fuzzy logic control had superior control stability and obstacle avoidance in an underwater environment.

scholarly journals Vehicle Door Opening Control Model Based on a Fuzzy Inference System to Prevent Motorcycle–Vehicle Door Crashes

2021 ◽  
Vol 13 (22) ◽  
pp. 12558
Author(s):  
Cheng-Yong Huang
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Inference System ◽  
Fuzzy Logic Control ◽  
Fuzzy Inference ◽  
Control Model ◽  
Inference System ◽  
Door Opening ◽  
Safety Distance ◽  
Logic Control

The goal of this research is to develop a fuzzy logic-based vehicle door control system to avoid motorcycle–vehicle door crash accidents. Accidents of this nature usually occur when the driver has parked the car, opens the door getting out of the car and collides with a motorcycle approaching from the rear, causing injury to the motorcyclist. In order to prevent such accidents, the fuzzy logic control system inputs the speed (MS) and safety distance (SD) of the motorcycle approaching from the rear, and then the fuzzy inference unit (FIU) calculates the clear output (Crisp) defuzzification Vehicle Door Opening Model (VDOM) value for the central locking system of the car, which can be used to trigger three modes, namely Danger Mode, Caution Mode and Warning Mode. In this study, the VDOM system is designed to trigger reasonable, reliable and consistent door control under different speeds of motorcycles coming from the rear and will be effectively applied to the door control of semi-automatic cars in the future.

scholarly journals Optimizing the wash time of the washing machine using several types of fuzzy numbers

2021 ◽  
Vol 45 (1) ◽  
pp. 105-116
Author(s):  
Md Azharul Islam ◽  
Md Sahadat Hossain
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Logic Control ◽  
Fuzzy Numbers ◽  
Fuzzy Inference ◽  
Washing Machine ◽  
Inference System ◽  
System A ◽  
Logic Control ◽  
Matlab Programming

In this paper we used various fuzzy numbers to calculate wash time for washing machines and provided various decisions after comparison among them. Again, this paper shows the importance of fuzzy logic control-based washing machine to get an appropriate wash time for the degree of dirt and the quantity of grease present on the clothes. This method is predicated on a fuzzy inference system. A fuzzy inference system using the Mamdani controller type is illustrated in this paper. From the utilization of fuzzy logic control, the machine can respond in several conditions. The simulation was done by MATLAB programming language. J. Bangladesh Acad. Sci. 45(1); 105-116: June 2021

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

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