  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.
