Journal
INFORMATION SCIENCES
Volume 451, Issue -, Pages 16-33Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2018.03.057
Keywords
Neural network-based control; Adaptive control; Unknown output nonlinearities; Robotic manipulator; Unknown dynamics
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Funding
- National Natural Science Foundation of China [61573108, U1501251]
- Natural Science Foundation of Guangdong Province [2016A030313715]
- Natural Science Foundation of Guangdong Province through the Science Fund for Distinguished Young Scholars [S20120011437]
- Ministry of Education of New Century Excellent Talent [NCET-12-0637]
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In this paper, the problem of neural network control for visual servoing robotic system is addressed, where the unmodeled dynamics and output nonlinearity are taken into account simultaneously. An adaptive neural network module is constructed to approach the unknown dynamics, upon which, the robot dynamics are not required to be linearly decomposable and structurally known. The major superiority of this module lies in its conciseness and the computational-reduction operation. Moreover, the output nonlinearity is considered, and its undesirable effect is subsequently tackled without a prior knowledge of the model parameters in output mechanism. It is proven by the Lyapunov method that the image-space tracking error is driven to an adjustable neighborhood of origin. Numerical simulations and experiments under various situations are used to validate the performance of the proposed adaptive neural network based scheme. (C) 2018 Elsevier Inc. All rights reserved.
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