Discrete-time decentralized neural backstepping controller for a five DOF robot manipulator

Author

Garcia-Hernandez, R. ; Sanchez, E.N. ; Saad, M. ; Bayro-Corrochano, E.

Author_Institution

Fac. de Ing., Univ. Autonoma del Carmen, Campeche, Mexico

fYear

2009

fDate

24-26 June 2009

Firstpage

552

Lastpage

557

Abstract

This paper deals with adaptive trajectory tracking for a five DOF robot manipulator, A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The HONN learning is performed online by an Extended Kalman Filter (EKF) algorithm. The applicability of the proposed scheme is illustrated via simulations.

Keywords

Kalman filters; feedback; manipulators; neurocontrollers; block strict feedback form; discrete-time decentralized neural backstepping controller; extended Kalman filter algorithm; high order neural network; robot manipulator; trajectory tracking; Adaptive control; Automatic control; Backstepping; Distributed control; Manipulator dynamics; Mobile robots; Neural networks; Programmable control; Robotics and automation; Trajectory; Backstepping; Extended Kalman Filter; High-order neural network; Robot Manipulator; Trajectory Tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2009. MED '09. 17th Mediterranean Conference on

Conference_Location

Thessaloniki

Print_ISBN

978-1-4244-4684-1

Electronic_ISBN

978-1-4244-4685-8

Type

conf

DOI

10.1109/MED.2009.5164600

Filename

5164600