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Conference Papers Year : 2021

Stabilization of sampled-data Lure systems with slope-restricted nonlinearities

Abstract

This paper addresses the stabilization of aperiodic sampled-data Lure systems, where the nonlinearity is assumed to be both sector and slope restricted. Based on a looped-functional and a Lure-type function, this method provides sufficient stabilization conditions in the form of matrix inequalities. It is shown that the proposed conditions guarantee that the Lure-type function is strictly decreasing at the sampling instants, which also implies that the continuous-time trajectories converge asymptotically to the origin. As the derived matrix inequalities are LMIs provided some variables are fixed, we propose a Particle Swarm Optimization (PSO) algorithm to compute a nonlinear sampled-data state feedback control law aiming at maximizing the intersampling interval or the sector bounds for which the global asymptotic stability of the origin of the closed-loop system is guaranteed.

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Dates and versions

hal-03453635 , version 1 (28-11-2021)

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Mathias Giordani Titton, João Manoel Gomes da Silva Jr., Giorgio Valmorbida, Marc Jungers. Stabilization of sampled-data Lure systems with slope-restricted nonlinearities. IEEE 60th Conference on Decision and Control (CDC 2021), Dec 2021, Austin, United States. ⟨10.1109/cdc45484.2021.9683646⟩. ⟨hal-03453635⟩
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