On control of discrete-time state-dependent jump linear systems with probabilistic constraints: A receding horizon approach

In this article, we consider a receding horizon control of discrete-time state-dependent jump linear systems, particular kind of stochastic switching systems, subject to possibly unbounded random disturbances and probabilistic state constraints. Due to a nature of the dynamical system and the constraints, we consider a one-step receding horizon. Using inverse cumulative distribution function, we convert the probabilistic state constraints to deterministic constraints, and obtain a tractable deterministic receding horizon control problem. We consider the receding control law to have a linear state-feedback and an admissible offset term. We ensure mean square boundedness of the state variable via solving linear matrix inequalities off-line, and solve the receding horizon control problem on-line with control offset terms. We illustrate the overall approach applied on a macroeconomic system

Output feedback H2 / Hinf control of a class of discrete-time reconfigurable control systems

International audienceIn this paper, static output feedback control of a class of discrete-time reconfigurable systems with Markovian parameters is addressed. The main contribution is to formulate conditions for multi-performance design related to this class of systems. The specifications and objectives under consideration include stochastic stability, H2 and H∞ performances. Another problematic related to a more general class of stochastic hybrid systems, known as Markovian Jump Linear Systems (MJLS), is also considered. This problematic concerns the mode-independent output feedback control of discrete-time MJLS. Results are formulated as nonlinear matrix inequalities. A numerical algorithm is provided and its running is illustrated on a classical example from literature

Bounded real lemma for nonhomogeneous markovian jump linear systems

International audienceIn this note, a bounded real lemma is established for discrete-time Markov jump linear systems with nonhomogeneous finite state Markov chain. Different cases, depending on the time variation character of the transition probabilities (TPs), are considered. Namely, arbitrary variation and periodic variation

Systèmes tolérant aux défauts : analyse et synthèse stochastiques

Despite the evident interaction between FDI and reconfiguration algorithms, it is true that the research on FDI and reconfiguration methods has often evolved separately, certainly because of the difficulty of each of these problems. The main contribution of this work is to use a mathematical model that includes in the same analysis framework the FDI and reconfiguration algorithms. Such a model belongs to the class of Markovian jump linear systems. In this class of systems, two random processes are defined: the first represents system components failures and the second represents the FDI process. The first problematic considered in this thesis is related to the synthesis of output feedback controllers that stochastically stabilize this class of systems subject to Brownian motion. The developed results are based essentially on Lyapunov theory and Supermartingale notion. The different synthesis conditions are formulated as nonlinear matrix inequalities problematic. Noncovex optimization algorithms were then proposed to solve these conditions. The second problematic addressed in this work concerns the multi-objective control of this class of Markovian jump systems. The specifications and objectives under consideration include stochastic stability, H_{2} and H_{infinity} performances. Output feedback controllers synthesis conditions were also proposed in term of LMI, BMI and NLMI. Finally, we have addressed the discrete-time counterpart and proposed H_{2}/H_{infinity} synthesis conditions. The developed results were applied to the problematic of control of networked systems subject to delays, packet loss and failures.Dans cette thèse, nous nous sommes intéressés aux contraintes résultants de l'intégration d'un module de diagnostic de pannes et d'un module de reconfiguration de lois de commandes. Contraintes pouvant conduire à une perte de performances, voir une instabilité, du système. La formalisation mathématique de cette problématique nous a amené à nous intéresser à une classe de systèmes hybrides stochastiques à sauts markoviens. La première partie du travail de thèse a été consacrée à la synthèse de lois de commande, par retour de sortie, stabilisant stochastiquement cette classe de systèmes à des bruits multiplicatifs. Les approches développées sont basées sur la théorie de Lyapunov et de Supermartingale. Les différentes conditions de synthèse sont données en termes d'inégalités matricielles non linéaires. Des algorithmes d'optimisation non convexe nt alors été proposés pour la résolution de ces différentes conditions. En deuxième partie de thèse, nous nous sommes intéressés au problème de commande multi-performances de cette classe de systèmes. Plus particulièrement, nous avons considéré des critères H_{infinity} et des critères H_{2}. Là aussi, nous avons proposé des conditions sous forme LMI, BMI et NLMI pour la résolution de ce problème. En dernière partie de thèse, nous nous sommes intéressés au cas des systèmes à temps discret. Nous avons là aussi considéré des problèmes de stabilisation stochastique et de commande multi-objectifs, pour lesquels des conditions sous forme LMI et NLMI ont été établies. Nous avons ensuite appliqué ces résultats à la problématique de commande de systèmes en réseaux sujets à des retards, des pertes de paquets et d'éventuels pannes

Event based estimation with correlated noises

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Event based estimation with correlated noises

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