Implementation of neural network based non-linear predictive

Research output: Research - peer-reviewArticle in proceedings – Annual report year: 1998

View graph of relations

The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system.
Original languageEnglish
Title of host publicationProc. of the 4th International Conference on Neural Network and Their Applications (NEURAP98)
Place of PublicationMarseilles, France
Publication date1998
Pages69-78
StatePublished - 1998
EventNEURAP'98 Fourth International Conference on Neural - Marseilles, France
Duration: 1 Jan 1998 → …

Conference

ConferenceNEURAP'98 Fourth International Conference on Neural
CityMarseilles, France
Period01/01/1998 → …
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

ID: 2961704