Real-time decentralized neural block controller for a robot manipulator

Authors:
R. Garcia-Hernandez;E. N. Sanchez;V. Santibañez;M. A. Llama;E. Bayro-Corrochano
Affiliations:
Universidad Autonoma del Carmen, Facultad de Ingenieria, Campeche, Mexico;CINVESTAV Guadalajara, Guadalajara, Jalisco, Mexico;Instituto Tecnologico de la Laguna, Torreon, Coahuila, Mexico;Instituto Tecnologico de la Laguna, Torreon, Coahuila, Mexico;CINVESTAV Guadalajara, Guadalajara, Jalisco, Mexico
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 4
Cited 0

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Abstract

This paper presents a discrete-time decentralized control scheme for identification and trajectory tracking of a two degrees of freedom (DOF) robot manipulator. A recurrent high order neural network (RHONN) structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The neural network learning is performed online by Kalman filtering. A controller is designed for each joint, using only local angular position and velocity measurements. These simple local joint controllers allow trajectory tracking with reduced computations. The proposed scheme is implemented in real-time to control a two DOF robot manipulator.