Journal ArticleDOI
A primal-dual neural network for online resolving constrained kinematic redundancy in robot motion control
Youshen Xia,Gang Feng,Jun Wang +2 more
- Vol. 35, Iss: 1, pp 54-64
TLDR
Simulation results show that the proposed primal-dual neural network has a faster convergence rate than the dual neural network in effectively tracking for the motion control of kinematically redundant manipulators.Abstract:
This paper proposes a primal-dual neural network with a one-layer structure for online resolution of constrained kinematic redundancy in robot motion control. Unlike the Lagrangian network, the proposed neural network can handle physical constraints, such as joint limits and joint velocity limits. Compared with the existing primal-dual neural network, the proposed neural network has a low complexity for implementation. Compared with the existing dual neural network, the proposed neural network has no computation of matrix inversion. More importantly, the proposed neural network is theoretically proved to have not only a finite time convergence, but also an exponential convergence rate without any additional assumption. Simulation results show that the proposed neural network has a faster convergence rate than the dual neural network in effectively tracking for the motion control of kinematically redundant manipulators.read more
Citations
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Journal ArticleDOI
Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks
TL;DR: Computer simulations are conducted and show that, compared to the existing methods, the proposed scheme can raise the manipulability almost 40% on average, which substantiates the efficacy, accuracy, and superiority of the proposed manipulable optimization scheme.
Journal ArticleDOI
Kinematic Control of Redundant Manipulators Using Neural Networks
Shuai Li,Yunong Zhang,Long Jin +2 more
TL;DR: This paper identifies two limitations of the existing RNN solutions for manipulator control, i.e., position error accumulation and the convex restriction on the projection set, and overcomes them by proposing two modified neural network models.
Journal ArticleDOI
Distributed Recurrent Neural Networks for Cooperative Control of Manipulators: A Game-Theoretic Perspective
TL;DR: This paper provides a tractable way to extend existing results on individual manipulator control using recurrent neural networks to the scenario with the coordination of multiple manipulators and proves global stability and solution optimality of the proposed neural networks.
Journal ArticleDOI
A One-Layer Recurrent Neural Network for Constrained Nonsmooth Optimization
Qingshan Liu,Jun Wang +1 more
TL;DR: This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as theNumber of decision variables of optimization problems.
Journal ArticleDOI
Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks
TL;DR: The global stability of the proposed neural network and the optimality of the neural solution are proven in theory and application orientated simulations demonstrate the effectiveness of this proposed method.
References
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