Adaptive recurrent neural control for robot trajectory tracking including friction

Author

Ricalde, Luis J. ; Sanchez, Edgar N. ; Perez, Jose P.

Author_Institution

CINVESTAV, Unidad Guadalajara, Jalisco, Mexico

Volume

1

fYear

2002

fDate

18-22 Nov. 2002

Firstpage

276

Abstract

The paper extends the results previously obtained for trajectory tracking of unknown plants using recurrent neural networks. The proposed controller structure, which consider systems with less inputs than states, is composed of a neural identifier and a control law defined by using the inverse optimal control approach. This new control scheme is applied to a robotic manipulator model, which includes friction terms.

Keywords

neurocontrollers; optimal control; position control; recurrent neural nets; robots; Lyapunov function; adaptive recurrent neural control; control law; controller structure; friction; inverse optimal control approach; neural identifier; recurrent neural networks; robot trajectory tracking; robotic manipulator model; stability analysis; trajectory tracking; unknown plants; Adaptive control; Friction; Manipulator dynamics; Neural networks; Optimal control; Programmable control; Recurrent neural networks; Robot control; Robot sensing systems; Trajectory;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on

Print_ISBN

981-04-7524-1

Type

conf

DOI

10.1109/ICONIP.2002.1202177

Filename

1202177