Simplified architecture of a type-2 fuzzy controller using four embedded type-1 fuzzy controllers and its application to a greenhouse climate control system

Author(s):  
I A Hameed

Uncertainty is an inherent part of control systems used in real-world applications. The use of new methods for handling incomplete information and to cope with large amounts of uncertainties is of fundamental importance. The traditional type-1 fuzzy sets (T1FSs) used in conventional fuzzy logic systems (FLSs) cannot fully handle the uncertainties present in control systems. The type-2 FSs (T2FSs) used in T2FLSs can handle such uncertainties in a better way because they provide more parameters and more degrees of freedom. In spite of the features of the T2FS, it has not received the attention it deserves because of its implementation complexity. In this paper, a simple architecture of the T2FLS using four embedded T1FLSs is introduced. The performance of the proposed T2FLS is assessed by applying it to a multi-input multi-output (MIMO) greenhouse climate control system. Simulation results showed that the simplified architecture is able to cope with the complexity of the plant and has the ability to handle measurement and modelling uncertainties. The performance of the proposed T2FLS is comparable with the computationally costly T2FLS and outperforms its traditional T1FLS counterpart.

2012 ◽  
Vol 10 (4) ◽  
pp. 926 ◽  
Author(s):  
C. Duarte-Galvan ◽  
I. Torres-Pacheco ◽  
R. G. Guevara-Gonzalez ◽  
R. J. Romero-Troncoso ◽  
L. M. Contreras-Medina ◽  
...  

2015 ◽  
Vol 15 (05) ◽  
pp. 1550083 ◽  
Author(s):  
SHAHRZAD GHOLAMI ◽  
ARIA ALASTY ◽  
HASSAN SALARIEH ◽  
MEHDI HOSSEINIAN-SARAJEHLOU

This paper deals with growth control of cancer cells population using type-1 and interval type-2 fuzzy logic. A type-1 fuzzy controller is designed in order to reduce the population of cancer cells, adjust the drug dosage in a manner that allows normal cells re-grow in treatment period and maintain the maximum drug delivery rate and plasma concentration of drug in an appropriate range. Two different approaches are studied. One deals with reducing the number of cancer cells without any concern about the rate of decreasing, and the other takes the rate of malignant cells damage into consideration. Due to the fact that uncertainty is an inherent part of real systems and affects controller efficacy, employing new methods of design such as interval type-2 fuzzy logic systems for handling uncertainties may be efficacious. Influence of noise on the system is investigated and the effect of altering free parameters of design is studied. Using an interval type-2 controller can diminish the effects of incomplete and uncertain information about the system, environmental noises, instrumentation errors, etc. Simulation results confirm the effectiveness of the proposed methods on tumor growth control.


2006 ◽  
Vol 94 (2) ◽  
pp. 165-177 ◽  
Author(s):  
Rodrigo Castañeda-Miranda ◽  
Eusebio Ventura-Ramos ◽  
Rebeca del Rocío Peniche-Vera ◽  
Gilberto Herrera-Ruiz

2020 ◽  
Vol 39 (5) ◽  
pp. 6089-6097
Author(s):  
Oscar Castillo ◽  
Prometeo Cortés-Antonio ◽  
Patricia Melin ◽  
Fevrier Valdez

This work presents a comparative analysis of Type-1 and Type-2 fuzzy controllers to drive an omnidirectional mobile robot in line-following tasks using line detection images. Image processing uses a Prewitt filter for edge detection and determines the error from the line location. The control systems are tested using four different paths from the Robotino® SIM simulator. Also, two different strategies in the design and implementation of the controllers are presented. In the first one, a PD controller scheme is extended by using a fuzzy system to have adaptive parameters P and D, additionally, Type-2 Fuzzy sets are used to give robustness to the controller. In the second case, a Fuzzy controller is designed to compute in a direct way the control variables and it is extended to Type-2 Fuzzy controller. Finally, experimental results and comparative analysis are presented for the five control schemes by comparing the running time and the standard deviation to measure the robustness of the control systems.


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