Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/73804
Title: Reference governor control of constrained feedback systems using neural networks
Authors: Jahagirdar, H.
Keerthi, S.S. 
Ang Jr., M.H. 
Issue Date: 2002
Source: 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
URI: http://scholarbank.nus.edu.sg/handle/10635/73804
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