Randomized and robust methods for uncertain systems using R-RoMulOC, with applications to DEMETER satellite benchmark

TL;DR

This paper introduces R-RoMulOC, a toolbox offering deterministic and probabilistic optimization methods for uncertain systems, demonstrated on the DEMETER satellite benchmark for analysis and control design.

Contribution

It presents the integration of robust and randomized optimization techniques into R-RoMulOC and applies them to a complex satellite benchmark for analysis and control.

Findings

Successful modeling of the DEMETER satellite within R-RoMulOC

Effective design of state-feedback controllers using the toolbox

Validation of probabilistic and deterministic methods for satellite control

Abstract

R-RoMulOC is a freely distributed toolbox which aims at making easily available to the users different optimization-based methods for dealing with uncertain systems. It implements both deterministic LMI-based results, that provide guaranteed performances for all values of the uncertainties, and probabilistic randomization-based approaches, that guarantee performances for all values of the uncertainties except for a subset with arbitrary small probability measure. The paper is devoted to the description of these two approaches for analysis and control design when applied to a satellite benchmark proposed by CNES, the French Space Agency. The paper also describes the modeling of the DEMETER satellite and its integration into the R-RoMulOC toolbox as a challenging test example. Design of state-feedback controllers and closed-loop performance analysis are carried out with the randomized and robust methods available in the R-RoMulOC toolbox.