Please use this identifier to cite or link to this item: https://doi.org/10.1109/41.649934
Title: Adaptive neural network control of robot manipulators in task space
Authors: Ge, S.S. 
Hang, C.C. 
Woon, L.C.
Keywords: Adaptive control
Neural network
Robot manipulator
Task space
Issue Date: 1997
Source: Ge, S.S.,Hang, C.C.,Woon, L.C. (1997). Adaptive neural network control of robot manipulators in task space. IEEE Transactions on Industrial Electronics 44 (6) : 746-752. ScholarBank@NUS Repository. https://doi.org/10.1109/41.649934
Abstract: In this paper, adaptive neural network control of robot manipulators in the task space is considered. The controller is developed based on a neural network modeling technique which neither requires the evaluation of inverse dynamical model nor the time-consuming training process. It is shown that, if Gaussian radial basis function networks are used, uniformly stable adaptation is assured, and asymptotically tracking is achieved. The controller thus obtained does not require the inverse of the Jacobian matrix. In addition, robust control can be easily incorporated to suppress the neural network modeling errors and the bounded disturbances. Numerical simulations are provided to show the effectiveness of the approach. © 1997 IEEE.
Source Title: IEEE Transactions on Industrial Electronics
URI: http://scholarbank.nus.edu.sg/handle/10635/61762
ISSN: 02780046
DOI: 10.1109/41.649934
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