Title :
Reference governor control of constrained feedback systems using neural networks
Author :
Jahagirdar, Harshad ; Keerthi, S. Sathiya ; Ang, M.H., Jr.
Author_Institution :
Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore
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.
Keywords :
backpropagation; control system synthesis; feedback; feedforward neural nets; linear systems; neurocontrollers; nonlinear control systems; state-space methods; constrained feedback systems; constrained system state-space; control variables; feedforward neural network architecture; linear control systems; neural networks; neural reference governor algorithm; nonlinear control systems; reference governor control; safety sets; state variables; two-layer feed-forward backpropagation neural network; Automatic control; Control systems; Equations; Mechanical variables control; Neural networks; Neurofeedback; Optimal control; Safety; Signal design; State feedback;
Conference_Titel :
Intelligent Control, 2002. Proceedings of the 2002 IEEE International Symposium on
Print_ISBN :
0-7803-7620-X
DOI :
10.1109/ISIC.2002.1157766
