We investigate robust stability of the fully probabilistic control with respect to data-driven model uncertainties. This scheme attempts to control a system modeled via a probability density function (pdf) and does so by computing a probabilistic control policy that is optimal in the Kullback-Leibler sense. The results are illustrated via simulations
: Given measured data we propose a model consisting of a linear, timeinvariant system affected by no...
Given measured data we propose a model consisting of a linear, time-invariant system affected by per...
Technical ReportRecently, probabilistic methods and statistical learning theory have been shown to p...
We investigate robust stability of the fully probabilistic control with respect to data-driven model...
we demonstrate several techniques to prove safety guarantees for robust control problems with statis...
Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistentl...
In this paper a novel generalised fully probabilistic controller design for the minimisation of the ...
This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject...
This paper presents a reliability- and robustness-based formulation for robust control synthesis for...
In this paper a new framework has been applied to the design of controllers which encompasses nonlin...
This thesis develops various methods for the robust and stochastic model-based control of uncertain ...
This paper presents a study on the optimization of systems with structured uncertainties, whose inpu...
Probability density function (PDF) control strategy investigates the controller design approaches wh...
In this work, the development of a probabilistic approach to robust control is motivated by structur...
The robustness of a control system for a plant with parametric uncertainties is, in a broad sense, i...
: Given measured data we propose a model consisting of a linear, timeinvariant system affected by no...
Given measured data we propose a model consisting of a linear, time-invariant system affected by per...
Technical ReportRecently, probabilistic methods and statistical learning theory have been shown to p...
We investigate robust stability of the fully probabilistic control with respect to data-driven model...
we demonstrate several techniques to prove safety guarantees for robust control problems with statis...
Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistentl...
In this paper a novel generalised fully probabilistic controller design for the minimisation of the ...
This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject...
This paper presents a reliability- and robustness-based formulation for robust control synthesis for...
In this paper a new framework has been applied to the design of controllers which encompasses nonlin...
This thesis develops various methods for the robust and stochastic model-based control of uncertain ...
This paper presents a study on the optimization of systems with structured uncertainties, whose inpu...
Probability density function (PDF) control strategy investigates the controller design approaches wh...
In this work, the development of a probabilistic approach to robust control is motivated by structur...
The robustness of a control system for a plant with parametric uncertainties is, in a broad sense, i...
: Given measured data we propose a model consisting of a linear, timeinvariant system affected by no...
Given measured data we propose a model consisting of a linear, time-invariant system affected by per...
Technical ReportRecently, probabilistic methods and statistical learning theory have been shown to p...