For a closed-loop system composed of a linear controlled plant and an MPC feedback strat-egy we show how to verify that closed-loop state trajectories either enter or avoid a given set of unsafe states. The search for the safety certificate is formulated as a mixed-integer linear programming problem which yields non-conservative certificates. The optimal control commands generated by the MPC policy are represented by Karush-Kuhn-Tucker optimality conditions, which allow to perform the verification without the need to explicitly compute reachable sets
A high-gain observer is used for a class of feedback linearisable nonlinear systems to synthesize sa...
International audienceIn this paper, we introduce a novel approach to safe learning-based Model Pred...
This paper presents a methodology for safety verification of continuous and hybrid systems in the wo...
Controller synthesis for nonlinear systems is considered with the following ob-jective: no trajector...
Controller synthesis for nonlinear systems is considered with the following objective: No trajectory...
Abstract — This paper presents a new approach for leveraging the power of theorem provers for formal...
This paper presents a new approach for leveraging the power of theorem provers for formal verificati...
[[abstract]]In an earlier paper, the authors introduced the notion of safety control of stochastic d...
Control systems designed via learning methods, aiming at quasi-optimal solutions, typically lack sta...
In this paper a robust MPC scheme based on a partial-state availability is developed for uncertain d...
Autonomous systems are often safety-critical and are expected to work in uncertain environments. En...
In this paper, we consider the control problem for uncertain systems with imperfect information, in ...
[[abstract]]In an earlier paper, the authors introduced the notion of safety control of stochastic d...
While distributed algorithms provide advantages for the control of complex large-scale systems by re...
The increasing impact of data-driven technologies across various industries has sparked renewed inte...
A high-gain observer is used for a class of feedback linearisable nonlinear systems to synthesize sa...
International audienceIn this paper, we introduce a novel approach to safe learning-based Model Pred...
This paper presents a methodology for safety verification of continuous and hybrid systems in the wo...
Controller synthesis for nonlinear systems is considered with the following ob-jective: no trajector...
Controller synthesis for nonlinear systems is considered with the following objective: No trajectory...
Abstract — This paper presents a new approach for leveraging the power of theorem provers for formal...
This paper presents a new approach for leveraging the power of theorem provers for formal verificati...
[[abstract]]In an earlier paper, the authors introduced the notion of safety control of stochastic d...
Control systems designed via learning methods, aiming at quasi-optimal solutions, typically lack sta...
In this paper a robust MPC scheme based on a partial-state availability is developed for uncertain d...
Autonomous systems are often safety-critical and are expected to work in uncertain environments. En...
In this paper, we consider the control problem for uncertain systems with imperfect information, in ...
[[abstract]]In an earlier paper, the authors introduced the notion of safety control of stochastic d...
While distributed algorithms provide advantages for the control of complex large-scale systems by re...
The increasing impact of data-driven technologies across various industries has sparked renewed inte...
A high-gain observer is used for a class of feedback linearisable nonlinear systems to synthesize sa...
International audienceIn this paper, we introduce a novel approach to safe learning-based Model Pred...
This paper presents a methodology for safety verification of continuous and hybrid systems in the wo...