Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64494
Title: Kinematic control of redundant manipulators using neural networks
Authors: Li, S 
Zhang, Y
Jin, L
Keywords: Robot arm.
Kinematic control
Neural network
Nonconvex set
Recurrent neural networks (RNNs)
Redundant manipulator
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2016, v. PP, no. 99, p. 1-12 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: Redundancy resolution is a critical problem in the control of robotic manipulators. Recurrent neural networks (RNNs), as inherently parallel processing models for time-sequence processing, are potentially applicable for the motion control of manipulators. However, the development of neural models for high-accuracy and real-time control is a challenging problem. 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. Our method allows nonconvex sets for projection operations, and control error does not accumulate over time in the presence of noise. Unlike most works in which RNNs are used to process time sequences, the proposed approach is model-based and training-free, which makes it possible to achieve fast tracking of reference signals with superior robustness and accuracy. Theoretical analysis reveals the global stability of a system under the control of the proposed neural networks. Simulation results confirm the effectiveness of the proposed control method in both the position regulation and tracking control of redundant PUMA 560 manipulators.
URI: http://hdl.handle.net/10397/64494
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2016.2574363
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