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Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Feb2018, Vol. 29 Issue 2, p419-426. 8p. - Publication Year :
- 2018
-
Abstract
- This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series–parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SERVOMECHANISMS
*ARTIFICIAL neural networks
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 29
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 127490705
- Full Text :
- https://doi.org/10.1109/TNNLS.2016.2628038