The concepts of ultimate bounds and invariant sets play a key role in several control theory problems, as they replace the notion of asymptotic stability in the presence of unknown disturbances. However, when the disturbances are unbounded, as in the case of Gaussian white noise, no ultimate bounds nor invariant sets can in general be found. To overcome this limitation we introduced, in previous work, the notions of probabilistic ultimate bound (PUB) and probabilistic invariant set (PIS) for discrete-time systems. This article extends the notions of PUB and PIS to continuous-time systems, studying their main properties and providing tools for their calculation. An application of these concepts to robust control design is also presented
Predictive control is a very useful tool in controlling constrained systems, since the constraints c...
International audienceThis paper proposes an invariant-set based minimal detectable fault (MDF) comp...
Abstract—Probabilistic discrete event systems (PDES) are modeled as generators of probabilistic lang...
The concepts of ultimate bounds and invariant sets play a key role in several control theory problem...
The concepts of ultimate bounds and invariant sets play a key role in several control theory problem...
The notions of invariant sets and ultimate bounds are important concepts in the analysis of dynamica...
Paper on stochastic invarianceInternational audienceIn this paper a constructive method to determine...
International audienceThe objective of this chapter is to present a methodology for computing robust...
Probabilistic (or quantitative) verification is a branch of formal methods dealing with stochastic m...
we demonstrate several techniques to prove safety guarantees for robust control problems with statis...
This thesis deals with the robust control of nonlinear systems subject to persistent bounded non-add...
[[abstract]]This paper is concerned with stability robustness bounds for those systems passing any o...
International audienceSince the early work of Lehoczky on real-time queuing theory, probabilistic ap...
Abstract: The problem of probability stability discrete-time control system is con-sidered. A method...
The concept of a continuous-time probabilistic automaton is presented in the paper. The probability ...
Predictive control is a very useful tool in controlling constrained systems, since the constraints c...
International audienceThis paper proposes an invariant-set based minimal detectable fault (MDF) comp...
Abstract—Probabilistic discrete event systems (PDES) are modeled as generators of probabilistic lang...
The concepts of ultimate bounds and invariant sets play a key role in several control theory problem...
The concepts of ultimate bounds and invariant sets play a key role in several control theory problem...
The notions of invariant sets and ultimate bounds are important concepts in the analysis of dynamica...
Paper on stochastic invarianceInternational audienceIn this paper a constructive method to determine...
International audienceThe objective of this chapter is to present a methodology for computing robust...
Probabilistic (or quantitative) verification is a branch of formal methods dealing with stochastic m...
we demonstrate several techniques to prove safety guarantees for robust control problems with statis...
This thesis deals with the robust control of nonlinear systems subject to persistent bounded non-add...
[[abstract]]This paper is concerned with stability robustness bounds for those systems passing any o...
International audienceSince the early work of Lehoczky on real-time queuing theory, probabilistic ap...
Abstract: The problem of probability stability discrete-time control system is con-sidered. A method...
The concept of a continuous-time probabilistic automaton is presented in the paper. The probability ...
Predictive control is a very useful tool in controlling constrained systems, since the constraints c...
International audienceThis paper proposes an invariant-set based minimal detectable fault (MDF) comp...
Abstract—Probabilistic discrete event systems (PDES) are modeled as generators of probabilistic lang...