Stabilising control for a class of chaotic systems based on adaptive fuzzy logic systems

2016 ◽  
Vol 3 (3) ◽  
pp. 165-178 ◽  
Author(s):  
Zilin Gao ◽  
Yinhe Wang ◽  
Jiang Xiong ◽  
Yong Pan
Keyword(s):  
Fuzzy Logic ◽  
Chaotic Systems ◽  
Fuzzy Logic Systems ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy Logic ◽  
Logic Systems

Output Stable Control Design for a Class of Nonlinear Systems Based on Adaptive Fuzzy Logic Systems

2014 ◽  
Vol 945-949 ◽  
pp. 2670-2675
Author(s):  
Zi Lin Gao ◽  
Yong Pan ◽  
Jiang Xiong ◽  
Jin Peng Chen
Keyword(s):  
Fuzzy Logic ◽  
Nonlinear Systems ◽  
Approximation Property ◽  
Control Design ◽  
Universal Approximation ◽  
Fuzzy Logic Systems ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy Logic ◽  
Logic Systems ◽  
Stable Control

The adaptive fuzzy logic systems are constructed in this paper by utilizing the data information sampled from the inputs and outputs of unknown functions in the nonlinear systems controlled, and then output stable controller is synthesized for a class of uncertain nonlinear systems based on the universal approximation property of adaptive fuzzy logic systems. Finally, the simulation shows the validity of the method in this paper.

scholarly journals Adaptive Fuzzy Control of Uncertain Robotic Manipulator

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jinglei Zhou ◽  
Qunli Zhang
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Controller ◽  
Mimo System ◽  
Lagrange Equation ◽  
Multiple Input Multiple Output ◽  
Adaptive Fuzzy Control ◽  
Robotic Manipulator ◽  
Fuzzy Logic Systems ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy Logic

This paper designs a kind of adaptive fuzzy controller for robotic manipulator considering external disturbances and modeling errors. First, n-link uncertain robotic manipulator dynamics based on the Lagrange equation is changed into a two-order multiple-input multiple-output (MIMO) system via feedback technique. Then, an adaptive fuzzy logic control scheme is studied by using sliding theory, which adopts the adaptive fuzzy logic systems to estimate the uncertainties and employs a filtered error to make up for the approximation errors, hence enhancing the robust performance of robotic manipulator system uncertainties. It is proved that the tracking errors converge into zero asymptotically by using Lyapunov stability theory. Last, we take a two-link rigid robotic manipulator as an example and give its simulations. Compared with the existing results in the literature, the proposed controller shows higher precision and stronger robustness.

Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification

2021 ◽  
pp. 014233122110066
Author(s):  
Yang Chen ◽  
Jiaxiu Yang
Keyword(s):  
Fuzzy Logic ◽  
Back Propagation ◽  
Membership Functions ◽  
Identification Problems ◽  
Fuzzy Logic Systems ◽  
Fuzzy Identification ◽  
Logic Systems ◽  
Academic Topic ◽  
Interval Type

In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.

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