Type-1 and Type-2 Fuzzy logic controller based Shunt active filter Id-Iq control strategy for mitigation of harmonics with Triangular membership function

2012 ◽  
Author(s):  
Suresh Mikkili ◽  
Anup Kumar Panda
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Control Strategy ◽  
Fuzzy Logic Controller ◽  
Active Filter ◽  
Shunt Active Filter ◽  
Triangular Membership Function

scholarly journals Application of Type-1 and Type-2 Fuzzy Logic Controller for The Real Swarm Robot

2019 ◽  
Vol 15 (06) ◽  
pp. 83 ◽  
Author(s):  
Ade Silvia Handayani ◽  
Nyayu Latifah Husni ◽  
Siti Nurmaini ◽  
Irsyadi Yani
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Complex Problem ◽  
The Real ◽  
Advantages And Disadvantages ◽  
Path Navigation ◽  
Swarm Robot ◽  
Interval Type

Navigation is one of the typical problem domains occurred in studying swarm robot. This task needs a special ability in avoiding obstacles.  This research presents the navigation techniques using type 1 fuzzy logic and interval type 2 fuzzy logic. A comparison of those two fuzzy logic performances in controlling swarm robot as tools for complex problem modeling, especially for path navigation is presented in this paper.  Each hierarchical of fuzzy logic shows its advantages and disadvantages.  For testing the robustness of type-1 fuzzy logic and interval type-2 fuzzy logic algorithms, 3 robots for the real swarm robot experiment are used.  Each is equipped with one compass sensor, three distance sensors, and one X-Bee communication module.  The experimental results show that type-2 fuzzy logic has better performance than type-1 fuzzy logic.

Interval Type-2 Fuzzy Logic Control of NM70 Shape Memory Actuator

2014 ◽  
Author(s):  
Ireneusz Dominik
Keyword(s):  
Fuzzy Logic ◽  
Shape Memory ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Logic Control ◽  
Interval Type ◽  
Nonlinear Plant

The main aim of this article is to present the usage of type-2 fuzzy logic controller to control a shape memory actuator. To enhance real-time performance simplified interval fuzzy sets were used. The algorithm was implemented in the ATmega32 microcontroller. The dedicated PC application was also built. The fuzzy logic controller type-2 was tested experimentally by controlling position of the shape memory alloy actuator NM70 which despite its small size distinguishes itself by its strength. The obtained results confirmed that type-2 fuzzy controller performed efficiently with a difficult to control nonlinear plant. The research also proved that interval type-2 controllers, which are a simplified version of the general type-2 controllers, are very efficient. They can handle uncertainties without increasing drastically the computational complexity. Experimental data comparison of the fuzzy logic controller type-2 with type-1 clearly indicates the superiority of the former, especially in reducing overshooting.

Implementation of the Type-2 Fuzzy Controller in PLC

2010 ◽  
Vol 164 ◽  
pp. 95-98 ◽  
Author(s):  
Ireneusz Dominik
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Research Work ◽  
Problem Type ◽  
Object A ◽  
Develop Type ◽  
Linear Object

The main aim of the presented research work was to develop type-2 fuzzy logic controller, which by its own design should be “more intelligent” than type-1. Along with the intelligence it should provide better results in solving a particular problem. Type-2 fuzzy logic controller is not well-known and it is rarely used at present. The idea of type-2 fuzzy logic set was presented by Zadeh in 1975, shortly after the presentation of type-1 fuzzy set. At the beginning scientists and researchers worked on type-1. Only after developing type-1 the attention was directed towards the type-2. The first applications of type-2 fuzzy logic in control appeared in 2003. The fuzzy logic controller type-2 was tested experimentally by controlling a non-linear object: a shape memory alloy (SMA) actuator DM-01PL, made by Miga Motor company, which despite small size distinguishes itself by its 9 N strength. Comparison of experimental data of the fuzzy logic controller type-2 and type-1 clearly indicates the superiority of the former, particularly in reducing signal overshoots.

scholarly journals Type-1 and Type-2 Fuzzy Logic Controller Based Multilevel DSTATCOM Using SVM*

2016 ◽  
Vol 25 (1) ◽  
Author(s):  
Foudil BENZERAFA ◽  
Abelhalim TLEMÇANI ◽  
Karim SEBAA
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller

scholarly journals Enhancement of Non-Holonomic Leader-Follower Formation Using Interval Type-2 Fuzzy Logic Controller

2018 ◽  
Vol 14 (09) ◽  
pp. 124 ◽  
Author(s):  
Bambang Tutuko ◽  
Siti Nurmaini ◽  
Saparudin Saparudin ◽  
Gita Fadila Fitriana
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Formation Control ◽  
Fuzzy Logic Controller ◽  
Small Data ◽  
Hardware Failure ◽  
Fuzzy Process ◽  
Interval Type

Robotics control system with leader-follower approach has a weakness in the case of formation failure if the leader robot fails. To overcome such problem, this paper proposes the formation control using Interval Type-2-Fuzzy Logic controller (IT2FLC). To validate the performance of the controller, simulations were performed with various environmental systems such as open spaces, complexes, circles and ovals with several parameters. The performance of IT2FLC will be compared with Type-1 Fuzzy Logic (T1FL) and Proportional Integral and Derivative (PID) controller. As the results found using IT2FLC has advantages in environmental uncertainty, sensor imprecision and inaccurate actuator. Moreover, IT2FLC produce good performance compared to T1FLC and PID controller in the above environments, in terms of small data generated in the fuzzy process, the rapid response of the leader robot to avoid collisions and stable movements of the follower robot to follow the leader's posture to reach the target without a crash. Especially in some situations when a leader robot crashes or stops due to hardware failure, the follower robot still continue move to the target without a collision.

scholarly journals Interval Type-2 Fuzzy Inference System Based on Closest Point of Approach for Collision Avoidance between Ships

2020 ◽  
Vol 10 (11) ◽  
pp. 3919 ◽  
Author(s):  
Sung Wook Ohn ◽  
Ho Namgung
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Collision Risk ◽  
Inference System ◽  
Interval Type

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( D C P A ) and Time to Closest Point of Approach ( T C P A ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size “±5%” (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments.

Sign in / Sign up

Export Citation Format

Share Document