Probabilistic set invariance and ultimate boundedness

DOI10.1016/j.automatica.2012.06.074zbMath1271.93139OpenAlexW2138700249MaRDI QIDQ361056

José A. De Doná, Ernesto Kofman, Maria Marta Seron

Publication date: 28 August 2013

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: http://www.sciencedirect.com/science/article/pii/S0005109812003408

zbMATH Keywords

linear systemsinvariant setsprobabilistic methodsultimate bounds

Mathematics Subject Classification ID

Discrete-time control/observation systems (93C55) Stochastic systems in control theory (general) (93E03)

Related Items (11)

Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets ⋮ Stochastic model predictive control for linear systems with unbounded additive uncertainties ⋮ Continuous-time probabilistic ultimate bounds and invariant sets: computation and assignment ⋮ Stochastic output feedback control: convex lifting approach ⋮ Probabilistic reachable and invariant sets for linear systems with correlated disturbance ⋮ Optimisation of transient and ultimate inescapable sets with polynomial boundaries for nonlinear systems ⋮ Confidence set-based analysis of minimal detectable fault under hybrid Gaussian and bounded uncertainties ⋮ Recursively feasible stochastic model predictive control using indirect feedback ⋮ H-infinity set-membership observer design for discrete-time LPV systems ⋮ Model predictive control suitable for closed-loop re-identification ⋮ Probabilistic ultimate bounds and invariant sets in nonlinear systems

Cites Work

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• Theory and computation of disturbance invariant sets for discrete-time linear systems
• Constrained control and estimation. An optimisation approach.
• Set invariance in control
• Research on probabilistic methods for control system design
• Feedback and invariance under uncertainty via set-iterates
• A systematic method to obtain ultimate bounds for perturbed systems
• Stabilization of stochastic nonlinear systems driven by noise of unknown covariance
• Invariant approximations of the minimal robust positively Invariant set
• Stochastic Tubes in Model Predictive Control With Probabilistic Constraints

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