Linear quadratic regulation using neural networks

Kevin L. Moore, Subbaram Naidu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The authors describe the use of neural networks for solving optimal control problems for discrete-time linear systems with quadratic cost functions. The result is obtained by formulating the optimal control problem as a quadratic programming problem with inequality constraints and then applying a result by M. Kennedy and L. Chua (1988). The authors present numerical examples of the method, comparisions to standard Ricatti equation solutions, and extensions to Kalman filtering and other applications, including real-time, adaptive optimal control. A result that makes it possible to use a neural net to solve optimization problems is described. It is shown how to formulate the linear quadratic regulator problem as a nonlinear programming problem. It is then possible to directly apply Kennedy and Chua's result to find the optimal control solution using a neural net.

Original languageEnglish (US)
Title of host publicationProceedings. IJCNN - International Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Pages735-739
Number of pages5
ISBN (Print)0780301641
StatePublished - 1992
Externally publishedYes
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: Jul 8 1991Jul 12 1991

Publication series

NameProceedings. IJCNN - International Joint Conference on Neural Networks

Other

OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA
Period7/8/917/12/91

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