Authors:
R. Garcia Hernandez
1
;
E. N. Sanchez
2
;
M. A. Llama
3
and
J. A. Ruz-Hernandez
1
Affiliations:
1
Universidad Autonoma del Carmen, Mexico
;
2
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico
;
3
Instituto Tecnologico de la Laguna, Mexico
Keyword(s):
High-order neural network, Extended Kalman filter, Backstepping, Trajectory tracking, Robot arm.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Architectures and Mechanisms
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper presents a discrete-time decentralized control strategy for trajectory tracking of a seven degrees of freedom (DOF) robot arm. 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 neural network learning is performed online by extended Kalman filter. The local controller for each joint use only local angular position and velocity measurements. The feasibility of the proposed scheme is illustrated via simulation.