Title :
Neural networks in control systems
Author :
Narendra, Kumpati S. ; Mukhopadhyay, Snehasis
Author_Institution :
Yale Univ., New Haven, CT, USA
Abstract :
Some of the problems that arise in the control of nonlinear systems in the presence of uncertainty are considered. Multilayer neural networks and radial basis function networks are used in the design of identifiers and controllers, and gradient methods are used to adjust their parameters. For a restricted class of nonlinear systems, it is shown that globally stable adaptive controllers can be determined. Simulation results are presented to demonstrate that the methods presented can be used for the effective control of complex nonlinear systems
Keywords :
adaptive control; feedforward neural nets; nonlinear control systems; stability; control systems; controller design; globally stable adaptive controllers; gradient methods; identifier design; multilayer neural networks; nonlinear systems; radial basis function networks; uncertainty; Adaptive control; Control systems; Gradient methods; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Radial basis function networks; Uncertainty;
Conference_Titel :
Decision and Control, 1992., Proceedings of the 31st IEEE Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-0872-7
DOI :
10.1109/CDC.1992.371803
