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Data-driven approximations of dynamical systems operators for control
Abstract The Koopman and Perron Frobenius transport operators are fundamentally
changing how we approach dynamical systems, providing linear representations for even …
changing how we approach dynamical systems, providing linear representations for even …
[ספר][B] Smart autonomous aircraft: flight control and planning for UAV
YB Sebbane - 2015 - books.google.com
Smart Autonomous Aircraft: Flight Control and Planning for UAV introduces the advanced
methods of flight control, planning, situation awareness, and decision making. This book is …
methods of flight control, planning, situation awareness, and decision making. This book is …
Koopman operator approach to optimal control selection under uncertainty
Uncertainty propagation is an important step in the derivation of optimal control strategies for
dynamic systems in the presence of state and parameter uncertainty. Many stochastic …
dynamic systems in the presence of state and parameter uncertainty. Many stochastic …
Mars entry navigation with uncertain parameters based on desensitized extended Kalman filter
Mars entry phase is the most challenging part among Mars entry, descent, and landing
(EDL). One of the main reasons is that the lander suffers tough tests from the uncertainties …
(EDL). One of the main reasons is that the lander suffers tough tests from the uncertainties …
A direction preserving discretization for computing phase-space densities
Ray flow methods are an efficient tool to estimate vibro-acoustic or electromagnetic energy
transport in complex domains at high-frequencies. Here, a Petrov--Galerkin discretization of …
transport in complex domains at high-frequencies. Here, a Petrov--Galerkin discretization of …
Efficient quadratures for high-dimensional Bayesian data assimilation
Bayesian update is a common strategy used to combine (uncertain) model predictions and
(noisy) observational data. A computational bottleneck in this data assimilation technique is …
(noisy) observational data. A computational bottleneck in this data assimilation technique is …
Model validation: A probabilistic formulation
This paper presents a probabilistic formulation of the model validation problem. The
proposed validation framework is simple, intuitive, and can account both deterministic and …
proposed validation framework is simple, intuitive, and can account both deterministic and …
Hypersonic entry vehicle state estimation using nonlinearity-based adaptive cubature Kalman filters
Guidance, navigation, and control of a hypersonic vehicle landing on the Mars rely on
precise state feedback information, which is obtained from state estimation. The high …
precise state feedback information, which is obtained from state estimation. The high …
Further results on probabilistic model validation in Wasserstein metric
In a recent work [1], we have introduced a probabilistic formulation for the model validation
problem to provide a unifying framework for (in) validating nonlinear deterministic and …
problem to provide a unifying framework for (in) validating nonlinear deterministic and …
Uncertainty quantification for stochastic nonlinear systems using Perron-Frobenius operator and Karhunen-Loève expansion
In this paper, a methodology for propagation of uncertainty in stochastic nonlinear dynamical
systems is investigated. The process noise is approximated using Karhunen-Loève (KL) …
systems is investigated. The process noise is approximated using Karhunen-Loève (KL) …