Fuzzy Logic-based Neural Modeling and Robust Control for Robot

Author

Liu, Zhi ; Zhang, Yun

Author_Institution

Dept. of Autom., Guangdong Univ. of Technol., Guangzhou

Volume

2

fYear

0

fDate

0-0 0

Firstpage

8976

Lastpage

8980

Abstract

A fuzzy logic-based neural modeling and robust control method is presented for biped robots. The dynamic inversion of biped robots is used to compensate the complex nonlinearity of the dynamic system and is approximated by the fuzzy neural network. To handle the construction errors of FNN, the H_infin controller is designed to attenuate the effects of the construction errors to a prescribed level. By integrating the H_infin method and the fuzzy logic-based neural modeling technique together, the proposed control method can guarantee the robust performance of the closed loop system. Simulation results show that the proposed method is effective

Keywords

H^infin control; adaptive control; closed loop systems; control nonlinearities; control system synthesis; fuzzy control; fuzzy neural nets; inverse problems; legged locomotion; neurocontrollers; nonlinear dynamical systems; robot dynamics; H_infin controller design; adaptive control; biped robots; closed loop system; dynamic inversion; dynamic system; fuzzy logic; fuzzy neural network; inverse system; neural modeling; nonlinearity compensation; robust control; Control systems; Error correction; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Legged locomotion; Orbital robotics; Robot control; Robotics and automation; Robust control; Adaptive control; Fuzzy neural networks; Inverse system method; Robotic control; Robust control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713736

Filename

1713736