Data-driven control of nonlinear systems from input-output data

TL;DR

This paper extends a data-driven control method for nonlinear systems to scenarios where only input-output data is available, enabling the design of dynamic output feedback controllers using semidefinite programming.

Contribution

It adapts a recent controller learning approach to input-output data, broadening its applicability for nonlinear system control.

Findings

Successfully extended the method to input-output data scenarios.

Enables design of dynamic output feedback controllers.

Maintains stability and linearization properties in the new setting.

Abstract

The design of controllers from data for nonlinear systems is a challenging problem. In a recent paper, De Persis, Rotulo and Tesi, "Learning controllers from data via approximate nonlinearity cancellation," IEEE Transactions on Automatic Control, 2023, a method to learn controllers that make the closed-loop system stable and dominantly linear was proposed. The approach leads to a simple solution based on data-dependent semidefinite programs. The method uses input-state measurements as data, while in a realistic setup it is more likely that only input-output measurements are available. In this note we report how the design principle of the above mentioned paper can be adjusted to deal with input-output data and obtain dynamic output feedback controllers in a favourable setting.