Adaptive fuzzy controller for robot navigation

Author

Godjevac, Jelena ; Steele, Nigel

Author_Institution

Microcomput. Lab., Swiss Federal Inst. of Technol., Lausanne, Switzerland

Volume

1

fYear

1996

fDate

8-11 Sep 1996

Firstpage

136

Abstract

In this paper an adaptive fuzzy controller based on the Takagi-Sugeno method is presented and a learning procedure for it is derived. First, it is shown that the Takagi-Sugeno controller can be modeled as a generalized (extended) form of the “conventional” radial basis function (RBF) network. A supervised learning procedure for such a controller is then developed. The learning capabilities were tested on the nonlinear function approximation problem and the results showed that the learning speed was higher than that of conventional RBF network. This adaptive controller was applied to the tasks of robot obstacle avoidance and wall following and satisfactory performance was also achieved

Keywords

adaptive control; feedforward neural nets; function approximation; fuzzy control; inference mechanisms; learning (artificial intelligence); mobile robots; navigation; path planning; Takagi-Sugeno method; adaptive fuzzy control; fuzzy inference; mobile robots; nonlinear function approximation; obstacle avoidance; radial basis function network; robot navigation; supervised learning; wall following; Adaptive control; Function approximation; Fuzzy control; Navigation; Programmable control; Radial basis function networks; Robot control; Supervised learning; Takagi-Sugeno model; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on

Conference_Location

New Orleans, LA

Print_ISBN

0-7803-3645-3

Type

conf

DOI

10.1109/FUZZY.1996.551732

Filename

551732