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Real-Time Decentralized Neural Control for a Five Dof Redundant Robot



This paper presents a discrete-time decentralized control scheme for trajectory tracking of a five degrees of freedom (DOF) redundant robot. A modified 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 block control and sliding mode technique. The neural network learning is performed on-line by Kalman filtering. The local controllers for each joint use only local angular position and velocity measurements. The proposed control scheme is implemented in real-time. The experimental results for trajectory tracking show the effectiveness of the proposed approach.



Total Pages: 15
Pages: 23-37


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Volume: 19
Issue: 1
Year: 2013

Cite this document


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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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