Abstract
We consider the problem of output regulation for the class of minimum-phase nonlinear systems described in normal form. We assume that the ideal steady state control input fulfills a nonlinear regression law that is linearly parametrized in the uncertain parameters and we propose an internal model-based design that combines high-gain and identification tools. The identification tool by which the internal model is updated, is discrete-time by thus obtaining a hybrid internal model. The present paper is part of a wider research activity of the authors in which the attempt is to combine high-gain tools typically used in the context of nonlinear output regulation with identification tools that are here used to estimate the optimal regression law by best fitting the friend and its time derivatives.
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