Work

Data-Driven Control of Nonlinear Systems from Input-Output Data

Year: 2023

Type: conference-paper

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.

Source: Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes

DOI: 10.1109/cdc49753.2023.10384071

Authors: X. Dai, C. De Persis, N. Monshizadeh, P. Tesi

Institutions: University of Groningen, University of Florence

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4391020613

FWCI: 3.595

Cites: 28

Cited by: 8

Related to: 10