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Recursive recurrent neural network: A novel model for manipulator control with different levels of physical constraints

Zhan Li Orcid Logo, Shuai Li Orcid Logo

CAAI Transactions on Intelligence Technology, Volume: 8, Issue: 3, Pages: 622 - 634

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1049/cit2.12125

Abstract

Manipulators actuate joints to let end effectors to perform precise path tracking tasks. Recurrent neural network which is described by dynamic models with parallel processing capability, is a powerful tool for kinematic control of manipulators. Due to physical limitations and actuation saturation o...

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Published in: CAAI Transactions on Intelligence Technology
ISSN: 2468-2322 2468-2322
Published: Institution of Engineering and Technology (IET) 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60977
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Abstract: Manipulators actuate joints to let end effectors to perform precise path tracking tasks. Recurrent neural network which is described by dynamic models with parallel processing capability, is a powerful tool for kinematic control of manipulators. Due to physical limitations and actuation saturation of manipulator joints, the involvement of joint constraints for kinematic control of manipulators is essential and critical. However, current existing manipulator control methods based on recurrent neural networks mainly handle with limited levels of joint angular constraints, and to the best of our knowledge, methods for kinematic control of manipulators with higher order joint constraints based on recurrent neural networks are not yet reported. In this study, for the first time, a novel recursive recurrent network model is proposed to solve the kinematic control issue for manipulators with different levels of physical constraints, and the proposed recursive recurrent neural network can be formulated as a new manifold system to ensure control solution within all of the joint constraints in different orders. The theoretical analysis shows the stability and the purposed recursive recurrent neural network and its convergence to solution. Simulation results further demonstrate the effectiveness of the proposed method in end-effector path tracking control under different levels of joint constraints based on the Kuka manipulator system. Comparisons with other methods such as the pseudoinverse-based method and conventional recurrent neural network method substantiate the superiority of the proposed method.
College: Faculty of Science and Engineering
Funders: This work is partially supported by the IMPACT Fund of Swansea University.
Issue: 3
Start Page: 622
End Page: 634