  The paper deals with the problem of output regulation in a "non-equilibrium"
context for a special class of multivariable nonlinear systems stabilizable by
high-gain feedback. A post-processing internal model design suitable for the
multivariable nature of the system, which might have more inputs than
regulation errors, is proposed. Uncertainties in the system and exosystem are
dealt with by assuming that the ideal steady state input belongs to a certain
"class of signals" by which an appropriate model set for the internal model can
be derived. The adaptation mechanism for the internal model is then cast as an
identification problem and a least square solution is specifically developed.
In line with recent developments in the field, the vision that emerges from the
paper is that approximate, possibly asymptotic, regulation is the appropriate
way of approaching the problem in a multivariable and uncertain context. New
insights about the use of identification tools in the design of adaptive
internal models are also presented.
