scholarly journals Fuzzy Dynamic Adaptation of Gap Generation and Mutation in Genetic Optimization of Type 2 Fuzzy Controllers

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Leticia Cervantes ◽  
Oscar Castillo ◽  
Denisse Hidalgo ◽  
Ricardo Martinez-Soto
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Flight Control ◽  
Fuzzy Systems ◽  
Performance Enhancement ◽  
Fuzzy Controller ◽  
Control Problems ◽  
Genetic Optimization ◽  
Two Parameters

We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems.

Disturbance Rejection of Interval Type-2 Fuzzy Systems Based on Equivalence-Input-Disturbance Approach

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
P. Selvaraj ◽  
R. Sakthivel ◽  
O. M. Kwon ◽  
M. Muslim
Keyword(s):  
Disturbance Rejection ◽  
Fuzzy Systems ◽  
Fuzzy Controller ◽  
Reference Signal ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Input Disturbance ◽  
Interval Type ◽  
Fuzzy State

This paper focuses on the problem of disturbance rejection for a class of interval type-2 (IT-2) fuzzy systems via equivalence-input-disturbance (EID)-based approach. The main objective of this work is to design a fuzzy state-feedback controller combined with a disturbance estimator such that the output of the fuzzy system perfectly tracks the given reference signal without steady-state error and produces an EID to eliminate the influence of the actual disturbances. By constructing a suitable Lyapunov function and using linear matrix inequality (LMI) technique, a new set of sufficient conditions is established in terms of linear matrix inequalities for the existence of fuzzy controller. Finally, a simple pendulum model is considered to illustrate the effectiveness and applicability of the proposed EID-based control design.

Lyapunov approach based design of a gain adaptive interval type-2 fuzzy controller for servo systems

2020 ◽  
pp. 1-19
Author(s):  
Ritu Rani De (Maity) ◽  
Rajani K. Mudi ◽  
Chanchal Dey
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Controller ◽  
Rule Base ◽  
Performance Study ◽  
Servo Systems ◽  
Lyapunov Approach ◽  
The Stability ◽  
Interval Type

This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a Quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.

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.

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