Stability of a neural predictive controller scheme on a neural model

Jim Benjamin Luther, Paul Haase Sørensen

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Abstract

In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model. The resulting controller is tested on a nonlinear pneumatic servo system.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Volume3
Publication date2009
Pages2087-2091
ISBN (Print)0-7803-5529-6
DOIs
Publication statusPublished - 2009
Event1999 IEEE International Joint Conference on Neural Networks - Washington, United States
Duration: 10 Jul 199916 Jul 1999

Conference

Conference1999 IEEE International Joint Conference on Neural Networks
CountryUnited States
CityWashington
Period10/07/199916/07/1999

Bibliographical note

Copyright: 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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