Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.
basic_science · Level V
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- Record sourced from PubMed, PMID 27913360.
- Also identified by DOI 10.1109/TNNLS.2016.2628038.
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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.