20,973 research outputs found
Rare Event Simulation and Splitting for Discontinuous Random Variables
Multilevel Splitting methods, also called Sequential Monte-Carlo or
\emph{Subset Simulation}, are widely used methods for estimating extreme
probabilities of the form where is a deterministic
real-valued function and can be a random finite- or
infinite-dimensional vector. Very often, is supposed to be
a continuous random variable and a lot of theoretical results on the
statistical behaviour of the estimator are now derived with this hypothesis.
However, as soon as some threshold effect appears in and/or is
discrete or mixed discrete/continuous this assumption does not hold any more
and the estimator is not consistent.
In this paper, we study the impact of discontinuities in the \emph{cdf} of
and present three unbiased \emph{corrected} estimators to handle them.
These estimators do not require to know in advance if is actually
discontinuous or not and become all equal if is continuous. Especially, one
of them has the same statistical properties in any case. Efficiency is shown on
a 2-D diffusive process as well as on the \emph{Boolean SATisfiability problem}
(SAT).Comment: 16 pages (12 + Appendix 4 pages), 6 figure
Quantum Multiobservable Control
We present deterministic algorithms for the simultaneous control of an
arbitrary number of quantum observables. Unlike optimal control approaches
based on cost function optimization, quantum multiobservable tracking control
(MOTC) is capable of tracking predetermined homotopic trajectories to target
expectation values in the space of multiobservables. The convergence of these
algorithms is facilitated by the favorable critical topology of quantum control
landscapes. Fundamental properties of quantum multiobservable control
landscapes that underlie the efficiency of MOTC, including the multiobservable
controllability Gramian, are introduced. The effects of multiple control
objectives on the structure and complexity of optimal fields are examined. With
minor modifications, the techniques described herein can be applied to general
quantum multiobjective control problems.Comment: To appear in Physical Review
Suspended Load Path Tracking Control Using a Tilt-rotor UAV Based on Zonotopic State Estimation
This work addresses the problem of path tracking control of a suspended load
using a tilt-rotor UAV. The main challenge in controlling this kind of system
arises from the dynamic behavior imposed by the load, which is usually coupled
to the UAV by means of a rope, adding unactuated degrees of freedom to the
whole system. Furthermore, to perform the load transportation it is often
needed the knowledge of the load position to accomplish the task. Since
available sensors are commonly embedded in the mobile platform, information on
the load position may not be directly available. To solve this problem in this
work, initially, the kinematics of the multi-body mechanical system are
formulated from the load's perspective, from which a detailed dynamic model is
derived using the Euler-Lagrange approach, yielding a highly coupled, nonlinear
state-space representation of the system, affine in the inputs, with the load's
position and orientation directly represented by state variables. A zonotopic
state estimator is proposed to solve the problem of estimating the load
position and orientation, which is formulated based on sensors located at the
aircraft, with different sampling times, and unknown-but-bounded measurement
noise. To solve the path tracking problem, a discrete-time mixed
controller with pole-placement constraints
is designed with guaranteed time-response properties and robust to unmodeled
dynamics, parametric uncertainties, and external disturbances. Results from
numerical experiments, performed in a platform based on the Gazebo simulator
and on a Computer Aided Design (CAD) model of the system, are presented to
corroborate the performance of the zonotopic state estimator along with the
designed controller
Continuous-Discrete Path Integral Filtering
A summary of the relationship between the Langevin equation,
Fokker-Planck-Kolmogorov forward equation (FPKfe) and the Feynman path integral
descriptions of stochastic processes relevant for the solution of the
continuous-discrete filtering problem is provided in this paper. The practical
utility of the path integral formula is demonstrated via some nontrivial
examples. Specifically, it is shown that the simplest approximation of the path
integral formula for the fundamental solution of the FPKfe can be applied to
solve nonlinear continuous-discrete filtering problems quite accurately. The
Dirac-Feynman path integral filtering algorithm is quite simple, and is
suitable for real-time implementation.Comment: 35 pages, 18 figures, JHEP3 clas
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