Please use this identifier to cite or link to this item:
|Title:||Reference governor control of constrained feedback systems using neural networks|
Ang Jr., M.H.
|Citation:||Jahagirdar, H.,Keerthi, S.S.,Ang Jr., M.H. (2002). Reference governor control of constrained feedback systems using neural networks. IEEE International Symposium on Intelligent Control - Proceedings : 223-227. ScholarBank@NUS Repository.|
|Abstract:||A neural network approach to reference governor control of systems with constraints on state and control variables is discussed. A feed-forward neural network architecture is used to define safety sets in a constrained system state-space. Results presented here include the description of a neural reference governor algorithm and its application to linear and nonlinear control systems. The objective is to demonstrate the feasibility of such a design as an alternative to the Lyapunov function approach to the control of constrained systems.|
|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 May 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.