Please use this identifier to cite or link to this item:
|Title:||Adaptive neural network control of robot manipulators in task space|
|Authors:||Ge, S.S. |
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 16, 2019
checked on Oct 27, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.