Open AccessAdaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System
Author(s) -
Mohammed A. A. Al-Mekhlafi,
Herman Wahid,
Azian Abd. Aziz
Publication year - 2018
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v8i5.pp3657-3665
Subject(s) - inverted pendulum , adaptive neuro fuzzy inference system , control theory (sociology) , controller (irrigation) , computer science , double inverted pendulum , matlab , fuzzy logic , nonlinear system , fuzzy control system , position (finance) , control engineering , engineering , control (management) , artificial intelligence , physics , quantum mechanics , finance , agronomy , economics , biology , operating system
The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.