Open AccessDirect adaptive general type‐2 fuzzy control for a class of uncertain non‐linear systems
Author(s) -
Ghaemi Mostafa,
HosseiniSani Seyyed Kamal,
Khooban Mohammad Hassan
Publication year - 2014
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2013.0185
Subject(s) - control theory (sociology) , fuzzy logic , controller (irrigation) , fuzzy control system , mathematics , defuzzification , fuzzy number , adaptive control , adaptive neuro fuzzy inference system , computer science , control engineering , fuzzy set , artificial intelligence , control (management) , engineering , biology , agronomy
In this study, a stable direct adaptive general type‐2 fuzzy logic controller (DAG2FLC) is introduced for a class of non‐linear systems. The proposed controller uses advantages of general type‐2 fuzzy logic systems (GT2FLSs) in handling dynamic uncertainties to approximate unknown non‐linear actions. Implementing general type‐2 fuzzy systems is computationally costly; however, by using a recently introduced α ‐plane representation, a GT2FLS can be seen as composition of several interval type‐2 fuzzy logic systems with a corresponding level of α for each. Linguistic rules are directly incorporated into the DAG2FLC controller and a H ∞ compensator is added to attenuate external disturbance and fuzzy approximation error. Also general type‐2 fuzzy adaptation laws are derived using Lyapunov approach, and the stability of the closed‐loop system has been proven by mathematical analysis. In order to evaluate the performance of the proposed controller, the results are compared with those obtained by direct adaptive type‐1 fuzzy logic controller and a direct adaptive interval type‐2 fuzzy logic controller, which are the latest researches in the problem in hand. The proposed controller is applied to a chaotic Gyro system as a case study. Simulation reveals the effectiveness of the proposed controller in presence of dynamic uncertainties and external disturbances.