Data-driven nonlinear output regulation via data-enforced incremental passivity

TL;DR

This paper introduces a data-driven nonlinear output regulation method that ensures asymptotic tracking of time-varying references and disturbance rejection by enforcing incremental passivity through data-dependent control design.

Contribution

It develops a novel data-driven feedback controller that guarantees incremental passivity for nonlinear systems with time-varying references and disturbances, using data-dependent LMIs.

Findings

Effective regulation of nonlinear systems demonstrated through numerical examples.

The method successfully stabilizes systems with unknown equilibrium inputs.

The approach integrates internal models with passivation feedback based on data.

Abstract

This work proposes a data-driven regulator design that drives the output of a nonlinear system asymptotically to a time-varying reference and rejects time-varying disturbances. The key idea is to design a data-driven feedback controller such that the closed-loop system is incrementally passive with respect to the regulation error and a virtual input. By carefully designing the virtual input, we solve the data-driven nonlinear output regulation problem where the reference and disturbances are generated by a linear exosystem. The designed regulator is composed of an internal model and a passivation feedback controller characterized by a set of data-dependent linear matrix inequalities. The proposed data-driven method is also applied to stabilizing the non-zero equilibrium of a class of nonlinear systems with unknown equilibrium input. Numerical examples are presented to illustrate the effectiveness of the proposed designs.