Output Regulation by Postprocessing Internal Models for a Class of Multivariable Nonlinear Systems

TL;DR

This paper introduces a novel output regulation method for multivariable nonlinear systems using a postprocessing internal model, allowing for more flexible control input dimensions and measurement utilization.

Contribution

It presents a new design paradigm employing a postprocessing internal model unit, handling larger control inputs and non-vanishing measurements, enhancing output regulation strategies.

Findings

Provides conditions for practical and asymptotic regulation

Shows the design of internal models is linked with stabilization

Handles control inputs larger than the number of regulated variables

Abstract

In this paper we propose a new design paradigm, which employing a postprocessing internal model unit, to approach the problem of output regulation for a class of multivariable minimum-phase nonlinear systems possessing a partial normal form. Contrary to previous approaches, the proposed regulator handles control inputs of dimension larger than the number of regulated variables, provided that a controllability assumption holds, and can employ additional measurements that need not to vanish at the ideal error-zeroing steady state, but that can be useful for stabilization purposes or to fulfil the minimum-phase requirement. Conditions for practical and asymptotic output regulation are given, underlying how in postprocessing schemes the design of internal models is necessarily intertwined with that of the stabilizer.