Real-time neuro-fuzzy inverse control applied to a DC motor

Author

Gonzalez-Gomez, J.C. ; Ruz-Hernandez, J.A. ; Garcia-Hernandez, R. ; Sanchez, E.N.

Author_Institution

Univ. Autonoma del Carmen, Ciudad del Carmen, Mexico

fYear

2011

fDate

26-28 Oct. 2011

Firstpage

1

Lastpage

5

Abstract

This paper describes the development of an inverse model for a direct current (DC) motor. The model consist of an Adaptive Network Fuzzy Inference System (ANFIS). The identification procedure includes: the experiment to collect data, ANFIS training and model validation in real-time. The obtained model is used to design a neuro-fuzzy inverse control strategy for trajectory tracking. The obtained real-time results are compared when an inverse ARX model is used for inverse control and to demonstrate that neuro-fuzzy strategy has a successful performance.

Keywords

DC motors; fuzzy control; machine control; neurocontrollers; real-time systems; ANFIS training; DC motor; adaptive network fuzzy inference system; direct current motor; inverse ARX model; inverse model; real-time neuro-fuzzy inverse control; trajectory tracking; Adaptation models; Angular velocity; DC motors; Data models; Mathematical model; Real time systems; Training; ANFIS; DC Motor; Inverse Model; Real-Time Control;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Engineering Computing Science and Automatic Control (CCE), 2011 8th International Conference on

Conference_Location

Merida City

Print_ISBN

978-1-4577-1011-7

Type

conf

DOI

10.1109/ICEEE.2011.6106631

Filename

6106631