In this paper we introduce the low-power high-gain observer, developed in [1], to solve problems of output regulation for nonlinear systems. We show how the new tool makes it possible the implementation of high dimensional controllers, that tipically arise when the ideal steady-state control that must be generated to secure zero regulation error is affected by uncertainties.
Bin, M., Astolfi, D., Marconi, L. (2016). Robust internal model design by nonlinear regression via low-power high-gain observers. Institute of Electrical and Electronics Engineers Inc. [10.1109/CDC.2016.7798992].
Robust internal model design by nonlinear regression via low-power high-gain observers
Bin, Michelangelo
Writing – Original Draft Preparation
;Astolfi, DanieleWriting – Review & Editing
;Marconi, LorenzoWriting – Review & Editing
2016
Abstract
In this paper we introduce the low-power high-gain observer, developed in [1], to solve problems of output regulation for nonlinear systems. We show how the new tool makes it possible the implementation of high dimensional controllers, that tipically arise when the ideal steady-state control that must be generated to secure zero regulation error is affected by uncertainties.File in questo prodotto:
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