Skip to Main content Skip to Navigation
Journal articles

Approximate nonlinear regulation via identification-based adaptive internal models

Abstract : This paper concerns the problem of adaptive output regulation for multivariable nonlinear systems in normal form. We present a regulator employing an adaptive internal model of the exogenous signals based on the theory of nonlinear Luenberger observers. Adaptation is performed by means of discrete-time system identification schemes, in which every algorithm fulfilling some optimality and stability conditions can be used. Practical and approximate regulation results are given relating the prediction capabilities of the identified model to the asymptotic bound on the regulated variables, which become asymptotic whenever a "right" internal model exists in the identifier's model set. The proposed approach, moreover, does not require "high-gain" stabilization actions.
Document type :
Journal articles
Complete list of metadata
Contributor : François Chaplais Connect in order to contact the contributor
Submitted on : Tuesday, November 24, 2020 - 3:16:51 PM
Last modification on : Sunday, January 30, 2022 - 6:22:54 PM

Links full text



Michelangelo Bin, Pauline Bernard, Lorenzo Marconi. Approximate nonlinear regulation via identification-based adaptive internal models. IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2021, 66 (8), ⟨10.1109/TAC.2020.3020563⟩. ⟨hal-03021893⟩



Record views