From Batch-to-Batch to online learning control: experimental motion control case study

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

Data-driven feedforward control can significantly improve the positioning performance of motion systems. The aim of this paper is to exploit the concept of batch-to-batch learning control with basis function, applied in an online fashion. This enables learning within a task while maintaining task flexibility. A recursive least squares optimization is proposed on the basis of input/output data to compute the optimal feedforward parameters. The proposed method is successfully validated in simulation, and applied to a benchmark motion system leading to a major performance improvement compared to only feedback control.
Originele taal-2Engels
TitelPreprints Joint Conference 8th IFAC Symposium on Mechatronic Systems (MECHATRONICS 2019), and 11th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2019) Vienna, Austria
Pagina's1013-1018
Aantal pagina's6
StatusGepubliceerd - 4 sep 2019
Evenement8th IFAC Symposium on Mechatronic Systems (MECHATRONICS 2019), and 11th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2019) Vienna, Austria - Vienna, Oostenrijk
Duur: 4 sep 20196 sep 2019
http://www.mechatronicsnolcos2019.org/

Congres

Congres8th IFAC Symposium on Mechatronic Systems (MECHATRONICS 2019), and 11th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2019) Vienna, Austria
LandOostenrijk
StadVienna
Periode4/09/196/09/19
Internet adres

Citeer dit

Mooren, N., Witvoet, G., & Oomen, T. (2019). From Batch-to-Batch to online learning control: experimental motion control case study. In Preprints Joint Conference 8th IFAC Symposium on Mechatronic Systems (MECHATRONICS 2019), and 11th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2019) Vienna, Austria (blz. 1013-1018)