Recurrent Neural Network for Kinematic Control of Redundant Manipulators With Periodic Input Disturbance and Physical Constraints | IEEE Journals & Magazine | IEEE Xplore

Recurrent Neural Network for Kinematic Control of Redundant Manipulators With Periodic Input Disturbance and Physical Constraints


Abstract:

Input disturbances and physical constraints are important issues in the kinematic control of redundant manipulators. In this paper, we propose a novel recurrent neural ne...Show More

Abstract:

Input disturbances and physical constraints are important issues in the kinematic control of redundant manipulators. In this paper, we propose a novel recurrent neural network to simultaneously address the periodic input disturbance, joint angle constraint, and joint velocity constraint, and optimize a general quadratic performance index. The proposed recurrent neural network applies to both regulation and tracking tasks. Theoretical analysis shows that, with the proposed neural network, the end-effector tracking and regulation errors asymptotically converge to zero in the presence of both input disturbance and the two constraints. Simulation examples and comparisons with an existing controller are also presented to validate the effectiveness and superiority of the proposed controller.
Published in: IEEE Transactions on Cybernetics ( Volume: 49, Issue: 12, December 2019)
Page(s): 4194 - 4205
Date of Publication: 13 August 2018

ISSN Information:

PubMed ID: 30106749

Funding Agency:


References

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