A neural-fuzzy walking control of an autonomous biped robot

Author

Ferreira, Joao P. ; Amaral, Tito G. ; Pires, Vitor Fernao ; Crisostomo, Manuel M. ; Coimbra, Paulo

Volume

15

fYear

2004

fDate

June 28 2004-July 1 2004

Firstpage

253

Lastpage

258

Abstract

In this paper, an adaptive neural-fuzzy walking control of an autonomous biped robot is proposed. This control system uses a feed forward neural network based on nonlinear regression. The general regression neural network is used to construct the base of an adaptive neuro-fuzzy system. The neural network uses an iterative grid partition method for the initial structure identification of the controller parameters. Comparison results are done between the proposed method and the ANFIS tool provided in the fuzzy MATLAB toolbox. The robot´s control system uses an inverted pendulum to balance of the gaits. The effectiveness of the proposed control system is demonstrated by simulation and experimental tests

Keywords

adaptive control; adaptive systems; digital simulation; feedforward neural nets; fuzzy control; fuzzy neural nets; fuzzy reasoning; fuzzy systems; iterative methods; legged locomotion; motion control; neurocontrollers; nonlinear control systems; pendulums; regression analysis; ANFIS tool; adaptive neurofuzzy system; adaptive neurofuzzy walking control; autonomous biped robot; controller parameters; feedforward neural network; fuzzy MATLAB toolbox; fuzzy reasoning; gait balance synthesis; inverted pendulum; iterative grid partition method; nonlinear regression; robot control system; structure identification; Adaptive control; Adaptive systems; Control systems; Feedforward neural networks; Feeds; Fuzzy neural networks; Legged locomotion; Neural networks; Nonlinear control systems; Programmable control;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation Congress, 2004. Proceedings. World

Conference_Location

Seville

Print_ISBN

1-889335-21-5

Type

conf

Filename

1438561