Approximate Nonlinear Regulation via Identification-Based Adaptive Internal Models

This article concerns the problem of adaptive output regulation for multivariable nonlinear systems in normal form. We present a regulator employing an internal model exogenous signals based on theory Luenberger observers. Adaptation is performed by means discrete-time system identification schemes, which every algorithm fulfilling some optimality and stability conditions can be used. Practical approximate results are given relating prediction capabilities identified to asymptotic bound regulated variables, become whenever “right” exists identifier's set. The proposed approach, moreover, does not require “high-gain” stabilization actions.