Please use this identifier to cite or link to this item:
|Title:||Robust model reference adaptive control of robots based on neural network parametrization|
|Authors:||Ge, S.S. |
|Citation:||Ge, S.S.,Lee, T.H. (1997). Robust model reference adaptive control of robots based on neural network parametrization. Proceedings of the American Control Conference 3 : 2006-2010. ScholarBank@NUS Repository.|
|Abstract:||In this paper, a robust model reference adaptive controller is presented for robots based on neural network parametrization. The controller is based on applying direct adaptive techniques to a basic fixed controller for better control performance, while a sliding mode control is introduced to guarantee robust closed-loop stability. It is shown that if Bounded Basis Function (BBF) networks are used for the parallel NN, uniformly stable adaptation is assured and asymptotic tracking of the reference signal is achieved.|
|Source Title:||Proceedings of the American Control Conference|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 23, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.