Functional learning in optimal non-linear control

L. Irlicht*, J. B. Moore

*Corresponding author for this work

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

Abstract

It is demonstrated that least squares functional learning can be successfully applied to the control of nonlinear plants when augmented with a plant linearization, in the case of stochastic or functional disturbances, and with unmodeled dynamics. However, as expected, the adaptive-Q scheme is more responsive to effects that are not state dependent. The learning-Q method with the interpolation functions on a 4 by 4 grid robustly controls the plant, but as expected, is surpassed by an adaptive scheme with learning enhancements. Work is currently being undertaken to improve the algorithm by means of truncation of the adaptation to those interpolation functions closest to the current region of interest to allow much finer grid spacings leading to further improvements in the control.

Original languageEnglish
Title of host publicationProceedings of the American Control Conference
Editors Anon
PublisherPubl by American Automatic Control Council
Pages2137-2142
Number of pages6
Volume3
ISBN (Print)0879425652
Publication statusPublished - 1991
Externally publishedYes
EventProceedings of the 1991 American Control Conference - Boston, MA, USA
Duration: 26 Jun 199128 Jun 1991

Other

OtherProceedings of the 1991 American Control Conference
CityBoston, MA, USA
Period26/06/9128/06/91

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    Irlicht, L., & Moore, J. B. (1991). Functional learning in optimal non-linear control. In Anon (Ed.), Proceedings of the American Control Conference (Vol. 3, pp. 2137-2142). Publ by American Automatic Control Council.