On-line adaptive control of robot manipulators using dynamic fuzzy neural networks

Yang Gao, M.J. Er, W.E. Leithead, D.J. Leith

Research output: Contribution to conferencePaper

5 Citations (Scopus)

Abstract

This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for motion control of a multilink robot manipulator. The proposed controller has the following salient features: (1) dynamic fuzzy neural networks structure, i.e. fuzzy control rules can be generated or deleted automatically, (2) adaptive learning, (3) online learning of the robot dynamics, (4) fast learning speed, and (5) fast convergence of tracking error. Global stability of the system is established using Lyapunov approach. Computer simulation studies of a two-link robot manipulator demonstrate that excellent tracking performance can be achieved under external disturbances
LanguageEnglish
Pages4828-4833
Number of pages5
Publication statusPublished - 27 Jun 2001
EventAmerican Control Conference - Arlington, VA, USA
Duration: 25 Jun 200127 Jun 2001

Conference

ConferenceAmerican Control Conference
CityArlington, VA, USA
Period25/06/0127/06/01

Fingerprint

Fuzzy neural networks
Manipulators
Robots
Controllers
Motion control
Fuzzy control
Computer simulation

Keywords

  • on-line
  • adaptive control
  • robot manipulators
  • dynamic
  • fuzzy neural networks

Cite this

Gao, Y., Er, M. J., Leithead, W. E., & Leith, D. J. (2001). On-line adaptive control of robot manipulators using dynamic fuzzy neural networks. 4828-4833. Paper presented at American Control Conference, Arlington, VA, USA, .
Gao, Yang ; Er, M.J. ; Leithead, W.E. ; Leith, D.J. / On-line adaptive control of robot manipulators using dynamic fuzzy neural networks. Paper presented at American Control Conference, Arlington, VA, USA, .5 p.
@conference{607f169dd9f24b39842e3e28fc873764,
title = "On-line adaptive control of robot manipulators using dynamic fuzzy neural networks",
abstract = "This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for motion control of a multilink robot manipulator. The proposed controller has the following salient features: (1) dynamic fuzzy neural networks structure, i.e. fuzzy control rules can be generated or deleted automatically, (2) adaptive learning, (3) online learning of the robot dynamics, (4) fast learning speed, and (5) fast convergence of tracking error. Global stability of the system is established using Lyapunov approach. Computer simulation studies of a two-link robot manipulator demonstrate that excellent tracking performance can be achieved under external disturbances",
keywords = "on-line , adaptive control, robot manipulators, dynamic , fuzzy neural networks",
author = "Yang Gao and M.J. Er and W.E. Leithead and D.J. Leith",
year = "2001",
month = "6",
day = "27",
language = "English",
pages = "4828--4833",
note = "American Control Conference ; Conference date: 25-06-2001 Through 27-06-2001",

}

Gao, Y, Er, MJ, Leithead, WE & Leith, DJ 2001, 'On-line adaptive control of robot manipulators using dynamic fuzzy neural networks' Paper presented at American Control Conference, Arlington, VA, USA, 25/06/01 - 27/06/01, pp. 4828-4833.

On-line adaptive control of robot manipulators using dynamic fuzzy neural networks. / Gao, Yang; Er, M.J.; Leithead, W.E.; Leith, D.J.

2001. 4828-4833 Paper presented at American Control Conference, Arlington, VA, USA, .

Research output: Contribution to conferencePaper

TY - CONF

T1 - On-line adaptive control of robot manipulators using dynamic fuzzy neural networks

AU - Gao, Yang

AU - Er, M.J.

AU - Leithead, W.E.

AU - Leith, D.J.

PY - 2001/6/27

Y1 - 2001/6/27

N2 - This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for motion control of a multilink robot manipulator. The proposed controller has the following salient features: (1) dynamic fuzzy neural networks structure, i.e. fuzzy control rules can be generated or deleted automatically, (2) adaptive learning, (3) online learning of the robot dynamics, (4) fast learning speed, and (5) fast convergence of tracking error. Global stability of the system is established using Lyapunov approach. Computer simulation studies of a two-link robot manipulator demonstrate that excellent tracking performance can be achieved under external disturbances

AB - This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for motion control of a multilink robot manipulator. The proposed controller has the following salient features: (1) dynamic fuzzy neural networks structure, i.e. fuzzy control rules can be generated or deleted automatically, (2) adaptive learning, (3) online learning of the robot dynamics, (4) fast learning speed, and (5) fast convergence of tracking error. Global stability of the system is established using Lyapunov approach. Computer simulation studies of a two-link robot manipulator demonstrate that excellent tracking performance can be achieved under external disturbances

KW - on-line

KW - adaptive control

KW - robot manipulators

KW - dynamic

KW - fuzzy neural networks

M3 - Paper

SP - 4828

EP - 4833

ER -

Gao Y, Er MJ, Leithead WE, Leith DJ. On-line adaptive control of robot manipulators using dynamic fuzzy neural networks. 2001. Paper presented at American Control Conference, Arlington, VA, USA, .