34,439 research outputs found
Distributed Set-Based Observers Using Diffusion Strategy
Distributed estimation is more robust against single points of failure and
requires less communication overhead compared to the centralized version. Among
distributed estimation techniques, set-based estimation has gained much
attention as it provides estimation guarantees for safety-critical applications
and copes with unknown but bounded uncertainties. We propose two distributed
set-based observers using interval-based and set-membership approaches for a
linear discrete-time dynamical system with bounded modeling and measurement
uncertainties. Both algorithms utilize a new over-approximating zonotopes
intersection step named the set-based diffusion step. We use the term diffusion
since our intersection of zonotopes formula resembles the traditional diffusion
step in the stochastic Kalman filter. Our new zonotopes intersection takes
linear time. Our set-based diffusion step decreases the estimation errors and
the size of estimated sets and can be seen as a lightweight approach to achieve
partial consensus between the distributed estimated sets. Every node shares its
measurement with its neighbor in the measurement update step. The neighbors
intersect their estimated sets constituting our proposed set-based diffusion
step. We represent sets as zonotopes since they compactly represent
high-dimensional sets, and they are closed under linear mapping and Minkowski
addition. The applicability of our algorithms is demonstrated by a localization
example. All used data and code to recreate our findings are publicly availabl
Nonlinear observation in fuel cell systems: a comparison between disturbance estimation and High-Order Sliding-Mode techniques
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper compares two Nonlinear Distributed Parameter Observers (NDPO) for the observation of a Proton Exchange Membrane Fuel Cell (PEMFC). Both NDPOs are based on the discretisation of distributed parameters models and they are used to estimate the state profile of gas concentrations in the anode and cathode gas channels of the PEMFC, giving detailed information about the internal conditions of the system. The reaction and water transport flow rates from the membrane to the channels are uncertainties of the observation problem and they are estimated throughout all the length of the PEMFC without the use of additional sensors. The first observation approach is a Nonlinear Disturbance Observer (NDOB) for the estimation of the disturbances in the NDPO. In the second approach, a novel implementation of a High-Order Sliding-Mode (HOSM) observer is developed to estimate the true value of the states as well as the reaction terms. The proposed observers are tested and compared through a simulation example at different operating points and their performance and robustness is analysed over a given case study, the New European Driving Cycle.Peer ReviewedPostprint (author's final draft
Observer Placement for Source Localization: The Effect of Budgets and Transmission Variance
When an epidemic spreads in a network, a key question is where was its
source, i.e., the node that started the epidemic. If we know the time at which
various nodes were infected, we can attempt to use this information in order to
identify the source. However, maintaining observer nodes that can provide their
infection time may be costly, and we may have a budget on the number of
observer nodes we can maintain. Moreover, some nodes are more informative than
others due to their location in the network. Hence, a pertinent question
arises: Which nodes should we select as observers in order to maximize the
probability that we can accurately identify the source? Inspired by the simple
setting in which the node-to-node delays in the transmission of the epidemic
are deterministic, we develop a principled approach for addressing the problem
even when transmission delays are random. We show that the optimal
observer-placement differs depending on the variance of the transmission delays
and propose approaches in both low- and high-variance settings. We validate our
methods by comparing them against state-of-the-art observer-placements and show
that, in both settings, our approach identifies the source with higher
accuracy.Comment: Accepted for presentation at the 54th Annual Allerton Conference on
Communication, Control, and Computin
Identification of weakly coupled multiphysics problems. Application to the inverse problem of electrocardiography
This work addresses the inverse problem of electrocardiography from a new
perspective, by combining electrical and mechanical measurements. Our strategy
relies on the defini-tion of a model of the electromechanical contraction which
is registered on ECG data but also on measured mechanical displacements of the
heart tissue typically extracted from medical images. In this respect, we
establish in this work the convergence of a sequential estimator which combines
for such coupled problems various state of the art sequential data assimilation
methods in a unified consistent and efficient framework. Indeed we ag-gregate a
Luenberger observer for the mechanical state and a Reduced Order Unscented
Kalman Filter applied on the parameters to be identified and a POD projection
of the electrical state. Then using synthetic data we show the benefits of our
approach for the estimation of the electrical state of the ventricles along the
heart beat compared with more classical strategies which only consider an
electrophysiological model with ECG measurements. Our numerical results
actually show that the mechanical measurements improve the identifiability of
the electrical problem allowing to reconstruct the electrical state of the
coupled system more precisely. Therefore, this work is intended to be a first
proof of concept, with theoretical justifications and numerical investigations,
of the ad-vantage of using available multi-modal observations for the
estimation and identification of an electromechanical model of the heart
SO(3)-invariant asymptotic observers for dense depth field estimation based on visual data and known camera motion
In this paper, we use known camera motion associated to a video sequence of a
static scene in order to estimate and incrementally refine the surrounding
depth field. We exploit the SO(3)-invariance of brightness and depth fields
dynamics to customize standard image processing techniques. Inspired by the
Horn-Schunck method, we propose a SO(3)-invariant cost to estimate the depth
field. At each time step, this provides a diffusion equation on the unit
Riemannian sphere that is numerically solved to obtain a real time depth field
estimation of the entire field of view. Two asymptotic observers are derived
from the governing equations of dynamics, respectively based on optical flow
and depth estimations: implemented on noisy sequences of synthetic images as
well as on real data, they perform a more robust and accurate depth estimation.
This approach is complementary to most methods employing state observers for
range estimation, which uniquely concern single or isolated feature points.Comment: Submitte
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A tutorial on cue combination and Signal Detection Theory: Using changes in sensitivity to evaluate how observers integrate sensory information
Many sensory inputs contain multiple sources of information (‘cues’), such as two sounds of different frequencies, or a voice heard in unison with moving lips. Often, each cue provides a separate estimate of the same physical attribute, such as the size or location of an object. An ideal observer can exploit such redundant sensory information to improve the accuracy of their perceptual judgments. For example, if each cue is modeled as an independent, Gaussian, random variable, then combining Ncues should provide up to a √N improvement in detection/discrimination sensitivity. Alternatively, a less efficient observer may base their decision on only a subset of the available information, and so gain little or no benefit from having access to multiple sources of information. Here we use Signal Detection Theory to formulate and compare various models of cue-combination, many of which are commonly used to explain empirical data. We alert the reader to the key assumptions inherent in each model, and provide formulas for deriving quantitative predictions. Code is also provided for simulating each model, allowing expected levels of measurement error to be quantified. Based on these results, it is shown that predicted sensitivity often differs surprisingly little between qualitatively distinct models of combination. This means that sensitivity alone is not sufficient for understanding decision efficiency, and the implications of this are discussed
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