Please use this identifier to cite or link to this item:
|Title:||Adaptive control for a class of nonlinear discrete-time systems using neural networks|
|Authors:||Ge, S.S. |
|Source:||Ge, S.S.,Li, G.Y.,Lee, T.H. (2001). Adaptive control for a class of nonlinear discrete-time systems using neural networks. IEEE International Symposium on Intelligent Control - Proceedings : 97-102. ScholarBank@NUS Repository.|
|Abstract:||In this paper, the adaptive control problem is studied for a class of discrete-time unknown nonlinear systems with general relative degree in the presence of bounded disturbances. To derive the feedback control, a causal state-space model of the plant is obtained. By using an NN observer to estimate the unavailable but predictable states of the system, a Lyapunov-based adaptive state feedback NN controller is proposed. The state feedback control avoids the possible singularity problem in adaptive nonlinear control. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). An arbitrarily small tracking error can be achieved if the size of neural networks is chosen large enough, and the control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.|
|Source Title:||IEEE International Symposium on Intelligent Control - Proceedings|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.