NDLI: Petri type 2 fuzzy neural networks approximator for adaptive control of uncertain non-linear systems

Content Provider	IET Digital Library
Author	Bibi, Youssouf Bouhali, Omar Bouktir, Tarek
Abstract	In this study, the authors developed a novel universal approximator by the integration of Petri networks into type 2 fuzzy neural networks (T2FNN). T2FNN involve large number of rules, which result in heavy computational burden and great computation time. By incorporating Petri layers to optimise the number of rules; these two drawbacks could be very well overcome. Moreover, a new inference type 2 fuzzy system was developed to reduce the time consumed in the iterative K–M inference procedure, and to increase the approximation accuracy. The proposed inference engine is based on the use of an adaptive modulation of the upper and the lower outputs. The Petri type 2 fuzzy neural networks (PT2FNN) approximator was used to approximate the adaptive control for uncertain single-input single-output non-linear system. The stability of the closed-loop system was proven and demonstrated using the Lyaponov approach. Comparative studies of the proposed PT2FNN approximator with type 1 fuzzy neural network and T2FNN were performed. The performances of PT2FNN over the two types of the fuzzy system were shown on the inverted pendulum system.
Starting Page	3130
Ending Page	3136
Page Count	7
ISSN	17518644
Volume Number	11
e-ISSN	17518652
Issue Number	Issue 17, Nov (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/11/17
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2017.0610
Journal	IET Control Theory & Applications
Publisher Date	2017-09-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Control Approximation Accuracy Approximation Theory Closed Loop System Closed-loop System Combinatorial Mathematics Fuzzy Control Fuzzy Neural Nets Fuzzy System Interpolation And Function Approximation Inverted Pendulum System Iterative K-M Inference Procedure Iterative Method Lyaponov Approach Lyapunov Method Neurocontrol Nonlinear Control System Numerical Analysis Pendulums Petri Nets Petri Type 2 Fuzzy Neural Network Approximator PT2FNN Approximator Self Adjusting Control System Stability in Control Theory Uncertain Single-input Single-output Nonlinear System
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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