In this letter, we introduce novel tractable approximations for robust Linear Matrix Inequality (LMI) problems. We present various Quadratic Matrix Inequalities (QMIs) that enable us to characterize the effect of ellipsoidal uncertainty in the robust problem. These formulations are expressed in terms of a set of auxiliary decision variables, which facilitate the derivation of a generalized S-procedure result. This generalization significantly reduces the conservatism of the obtained results, compared with conventional approaches.

Tractable Approximations of LMI Robust Feasibility Sets

Mammarella, Martina
Secondo
;
Dabbene, Fabrizio
Penultimo
;
2024

Abstract

In this letter, we introduce novel tractable approximations for robust Linear Matrix Inequality (LMI) problems. We present various Quadratic Matrix Inequalities (QMIs) that enable us to characterize the effect of ellipsoidal uncertainty in the robust problem. These formulations are expressed in terms of a set of auxiliary decision variables, which facilitate the derivation of a generalized S-procedure result. This generalization significantly reduces the conservatism of the obtained results, compared with conventional approaches.
2024
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni - IEIIT
Linear matrix inequalities, Quadratic matrix inequality, Robust semidefinite programming
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/496401
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