Approximate nonlinear regulation via identification-based adaptive internal models

Michelangelo Bin (1) , Pauline Bernard (2) , Lorenzo Marconi (3)

1 DEEE - Department of Electrical and Electronic Engineering [London]
2 CAS - Centre Automatique et Systèmes
3 CASY-DEIS - Center for Research on Complex Automated Systems

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.

Mots clés: Output Regulation; Identification for Control; Adaptive Systems; Nonlinear Control; Internal Model

Domaines: Automatique / Robotique

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