Please use this identifier to cite or link to this item:
|Title:||Adaptive NN control for a class of discrete-time non-linear systems|
|Authors:||Ge, S.S. |
|Citation:||Ge, S.S., Lee, T.H., Li, G.Y., Zhang, J. (2003-03-10). Adaptive NN control for a class of discrete-time non-linear systems. International Journal of Control 76 (4) : 334-354. ScholarBank@NUS Repository. https://doi.org/10.1080/0020717031000073063|
|Abstract:||In this paper, adaptive neural network (NN) control is investigated for a class of single-input single-output (SISO) discrete-time unknown non-linear systems with general relative degree in the presence of bounded disturbances. Firstly, the systems are transformed into a causal state space description, adaptive state feedback NN control is presented based on Lyapunov's stability theory. Then, by converting the systems into a causal input-output representation, adaptive output feedback NN control is given. Finally, adaptive NN observer design and observer-based adaptive control are presented under the assumption of persistent excitation (PE). All the control schemes avoid the so-called controller singularity problem in adaptive control. By suitably choosing the design parameters, the closed-loop systems are proven to be semi-globally uniformly ultimately bounded (SGUUB). Simulation studies show the effectiveness of the newly proposed schemes.|
|Source Title:||International Journal of Control|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 18, 2018
WEB OF SCIENCETM
checked on May 30, 2018
checked on Jul 6, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.