A fuzzy-Gaussian neural network and its application to mobile robot control

Keigo Watanabe, Jun Tang, Masatoshi Nakamura, Shinji Koga, Toshio Fukuda

Research output: Contribution to journalArticle

69 Citations (Scopus)

Abstract

A fuzzy-Gaussian neural network (FGNN) controller is described by applying a Gaussian function as an activation function. A specialized learning architecture is used so that membership function can be tuned without using expert's manipulated data. As an example of the application, a tracking control problem for the speed and azimuth of a mobile robot driven by two independent wheels is solved by using the FGNN controller. To simplify the implementation of the FGNN controller for the two-input/two-output controlled system, a learning controller is utilized consisting of two FGNN's based on independent reasoning and a connection net with fixed weights. The effectiveness of the proposed method is illustrated by performing the simulation of a circular or square trajectory tracking control.

Original languageEnglish
Pages (from-to)193-199
Number of pages7
JournalIEEE Transactions on Control Systems Technology
Volume4
Issue number2
DOIs
Publication statusPublished - 1996
Externally publishedYes

Fingerprint

Mobile robots
Neural networks
Controllers
Membership functions
Wheels
Chemical activation
Trajectories

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

A fuzzy-Gaussian neural network and its application to mobile robot control. / Watanabe, Keigo; Tang, Jun; Nakamura, Masatoshi; Koga, Shinji; Fukuda, Toshio.

In: IEEE Transactions on Control Systems Technology, Vol. 4, No. 2, 1996, p. 193-199.

Research output: Contribution to journalArticle

Watanabe, Keigo ; Tang, Jun ; Nakamura, Masatoshi ; Koga, Shinji ; Fukuda, Toshio. / A fuzzy-Gaussian neural network and its application to mobile robot control. In: IEEE Transactions on Control Systems Technology. 1996 ; Vol. 4, No. 2. pp. 193-199.
@article{8d54b9953ff042d78acae246118c2e35,
title = "A fuzzy-Gaussian neural network and its application to mobile robot control",
abstract = "A fuzzy-Gaussian neural network (FGNN) controller is described by applying a Gaussian function as an activation function. A specialized learning architecture is used so that membership function can be tuned without using expert's manipulated data. As an example of the application, a tracking control problem for the speed and azimuth of a mobile robot driven by two independent wheels is solved by using the FGNN controller. To simplify the implementation of the FGNN controller for the two-input/two-output controlled system, a learning controller is utilized consisting of two FGNN's based on independent reasoning and a connection net with fixed weights. The effectiveness of the proposed method is illustrated by performing the simulation of a circular or square trajectory tracking control.",
author = "Keigo Watanabe and Jun Tang and Masatoshi Nakamura and Shinji Koga and Toshio Fukuda",
year = "1996",
doi = "10.1109/87.486346",
language = "English",
volume = "4",
pages = "193--199",
journal = "IEEE Transactions on Control Systems Technology",
issn = "1063-6536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

TY - JOUR

T1 - A fuzzy-Gaussian neural network and its application to mobile robot control

AU - Watanabe, Keigo

AU - Tang, Jun

AU - Nakamura, Masatoshi

AU - Koga, Shinji

AU - Fukuda, Toshio

PY - 1996

Y1 - 1996

N2 - A fuzzy-Gaussian neural network (FGNN) controller is described by applying a Gaussian function as an activation function. A specialized learning architecture is used so that membership function can be tuned without using expert's manipulated data. As an example of the application, a tracking control problem for the speed and azimuth of a mobile robot driven by two independent wheels is solved by using the FGNN controller. To simplify the implementation of the FGNN controller for the two-input/two-output controlled system, a learning controller is utilized consisting of two FGNN's based on independent reasoning and a connection net with fixed weights. The effectiveness of the proposed method is illustrated by performing the simulation of a circular or square trajectory tracking control.

AB - A fuzzy-Gaussian neural network (FGNN) controller is described by applying a Gaussian function as an activation function. A specialized learning architecture is used so that membership function can be tuned without using expert's manipulated data. As an example of the application, a tracking control problem for the speed and azimuth of a mobile robot driven by two independent wheels is solved by using the FGNN controller. To simplify the implementation of the FGNN controller for the two-input/two-output controlled system, a learning controller is utilized consisting of two FGNN's based on independent reasoning and a connection net with fixed weights. The effectiveness of the proposed method is illustrated by performing the simulation of a circular or square trajectory tracking control.

UR - http://www.scopus.com/inward/record.url?scp=0030109549&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030109549&partnerID=8YFLogxK

U2 - 10.1109/87.486346

DO - 10.1109/87.486346

M3 - Article

VL - 4

SP - 193

EP - 199

JO - IEEE Transactions on Control Systems Technology

JF - IEEE Transactions on Control Systems Technology

SN - 1063-6536

IS - 2

ER -