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Updated: Jul 7, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System
Published on: October 1, 2019
A dual neural network for redundancy resolution of kinematically redundant manipulators subject to joint limits and
Yunong Zhang1, Jun Wang, Youshen Xia
1Dept. of Autom. and Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China.
Abstract:
In this paper, a recurrent neural network called the dual neural network is proposed for online redundancy resolution of kinematically redundant manipulators. Physical constraints such as joint limits and joint velocity limits, together with the drift-free criterion as a secondary task, are incorporated into the problem formulation of redundancy resolution. Compared to other recurrent neural networks, the dual neural network is piecewise linear and has much simpler architecture with only one layer of neurons. The dual neural network is shown to be globally (exponentially) convergent to optimal solutions. The dual neural network is simulated to control the PA10 robot manipulator with effectiveness demonstrated.
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