Adaptive fuzzy logic controller for vehicle active suspensions with interval type-2 fuzzy membership functions

2008 ◽  
Author(s):  
Jiangtao Cao ◽  
Honghai Liu ◽  
Ping Li ◽  
David Brown
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Active Suspensions ◽  
Adaptive Fuzzy ◽  
Fuzzy Membership Functions ◽  
Adaptive Fuzzy Logic ◽  
Interval Type

scholarly journals COMPARISON OF FUZZY LOGIC CONTROLLERS FOR A MULTIVARIABLE PROCESS

2014 ◽  
pp. 138-143
Author(s):  
KARTHICK S ◽  
Dr.P. Lakshmi ◽  
DEEPA T
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dynamic Performance ◽  
Absolute Error ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Error Performance ◽  
Fuzzy Membership Functions ◽  
Interval Type

The Interval Type-2 Fuzzy Logic Controller (IT2FLC) for a Quadruple Tank Process (QTP) is demonstrated in this paper. Here the Interval Type-2 based Fuzzy membership function is used. The QTP is made to operate in minimum phase mode. The vertices of fuzzy membership functions are tuned with IT2FLC to minimize Integral Absolute Error. Performance of IT2FLC and Type-1 Fuzzy Logic Controller (T1FLC) are compared with decentralized PI controller, by simulation using MATLAB/Simulink. Simulation results show that satisfactory performance for both servo and regulatory responses.It has been observed that dynamic performance of IT2FLC is better than the other two controllers. Moreover, compared with the T1FLC controller, IT2FLC performs better, particularly in noisy environments.

scholarly journals Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems

Electronics ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 189 ◽  
Author(s):  
Aryuanto Soetedjo ◽  
Yusuf Nakhoda ◽  
Choirul Saleh
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Cost Reduction ◽  
Fuzzy Logic Controller ◽  
Management Systems ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Energy Management Systems ◽  
Fuzzy Membership Functions ◽  
Home Energy Management

Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%.

scholarly journals Automatic Methods for Generation of Type-1 and Interval Type-2 Fuzzy Membership Functions

2015 ◽  
Vol 11 (9) ◽  
pp. 976-987 ◽  
Author(s):  
Andréia Alves dos Santos Schwaab ◽  
Silvia Modesto Nassar ◽  
Paulo José de Freitas Filho
Keyword(s):  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Automatic Methods ◽  
Interval Type

scholarly journals Design of Fuzzy Membership Functions for Predicting Student’s Knowledge Performance

2019 ◽  
Vol 3 (6) ◽  
Author(s):  
Salisu Muhammad Sani
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Problem Solving ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Membership ◽  
Linguistic Variables ◽  
Membership Functions ◽  
Fuzzy Membership Functions

A Fuzzy logic controller is a problem-solving control system that provides means for representing approximate knowledge. The output of a fuzzy controller is derived from the fuzzifications of crisp (numerical) inputs using associated membership functions. The crisp inputs are usually converted to the different members of the associated linguistic variables based on their respective values. This point is evident enough to show that the output of a fuzzy logic controller is heavily dependent on its memberships of the different membership functions, which can be considered as a range of inputs [4]. Input membership functions can take various forms trapezoids, triangles, bell curves, singleton or any other shape that accurately enables the distribution of information within the system, in as much as the shape provides a region of transition between adjacent membership functions.

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