Add to ORKGAbstract: This paper addresses computations of a robustly safe region on the state space for uncertain constrained systems subject to disturbances based on a probabilistic approach. We first define a probabilistic output admissible (POA) set. This set is a subset of the state space which excludes with high probability initial states violating the constraint. Then, an algorithm for computing the POA set is developed based on a randomized technique. The utility of the POA set is demonstrated through a numerical simulation. 1
The "scenario approach" provides an intuitive method to address chance constrained problems arising ...
This work studies the combination of safe and probabilistic reasoning through the hybridization of M...
State-space reduction for probabilistic model checking Description Model-checking is a popular verif...
This thesis is concerned with analysis and control of systems with state and input constraints (cons...
Constraint programming has been used in many applica-tions where uncertainty arises to model safe re...
Probabilistic logic models are used ever more often to deal with the uncertain relations typical of ...
This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject...
This paper proposes a probabilistic solution framework for robust control analysis and synthesis pro...
This thesis deals with the topic of min-max formulations of robust model predictive control problems...
This paper proposes a method of approximating positively invariant sets and n-step controllable sets...
International audienceThis paper considers discrete-time, uncertain Piecewise Affine (PWA) systems a...
This paper proposes a new probabilistic solution framework for robust control analysis and synthesis...
Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions...
The role played by counterexamples in standard system analysis is well known; but less common is a n...
Abstract. This paper introduces a probabilistic extension of UML statecharts. A requirements-level s...
The "scenario approach" provides an intuitive method to address chance constrained problems arising ...
This work studies the combination of safe and probabilistic reasoning through the hybridization of M...
State-space reduction for probabilistic model checking Description Model-checking is a popular verif...
This thesis is concerned with analysis and control of systems with state and input constraints (cons...
Constraint programming has been used in many applica-tions where uncertainty arises to model safe re...
Probabilistic logic models are used ever more often to deal with the uncertain relations typical of ...
This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject...
This paper proposes a probabilistic solution framework for robust control analysis and synthesis pro...
This thesis deals with the topic of min-max formulations of robust model predictive control problems...
This paper proposes a method of approximating positively invariant sets and n-step controllable sets...
International audienceThis paper considers discrete-time, uncertain Piecewise Affine (PWA) systems a...
This paper proposes a new probabilistic solution framework for robust control analysis and synthesis...
Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions...
The role played by counterexamples in standard system analysis is well known; but less common is a n...
Abstract. This paper introduces a probabilistic extension of UML statecharts. A requirements-level s...
The "scenario approach" provides an intuitive method to address chance constrained problems arising ...
This work studies the combination of safe and probabilistic reasoning through the hybridization of M...
State-space reduction for probabilistic model checking Description Model-checking is a popular verif...