Trajectory synthesis based on different fuzzy modeling controllers

Author

Juang, Jih-Gau

Author_Institution

Inst. of Maritime Technol., Nat. Taiwan Ocean Univ., Keelung, Taiwan

Volume

5

fYear

2000

fDate

2000

Firstpage

3417

Abstract

Trajectory synthesis based on different fuzzy modeling controllers is investigated. The learning scheme uses a fuzzy modeling neural network as the controller. Simulations focus on refinement and a thorough understanding of the fuzzy modeling structure. Given prespecified constraints such as the step length, crossing clearance, and walking speed, the learning scheme can generate a trajectory that satisfies these constraints. Different issues regarding the scheme are examined. They include the effects of using different membership functions and the effects of using different network structures. Simulation results are reported for a five-link biped robot

Keywords

fuzzy control; fuzzy neural nets; learning (artificial intelligence); legged locomotion; multilayer perceptrons; position control; crossing clearance; five-link biped robot; fuzzy modeling controllers; fuzzy modeling neural network; learning scheme; membership functions; network structures; step length; trajectory synthesis; walking speed; Fuzzy control; Fuzzy neural networks; Fuzzy set theory; Leg; Legged locomotion; Marine technology; Network synthesis; Neural networks; Oceans; Robots;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2000. Proceedings of the 2000

Conference_Location

Chicago, IL

ISSN

0743-1619

Print_ISBN

0-7803-5519-9

Type

conf

DOI

10.1109/ACC.2000.879202

Filename

879202