All Years Seminars
[INMA] 2010-09-28 (15.00) : A new framework for stability analysis of networked control systems
At Euler building
Speaker :
Ishido YUMIKO (Université de Kyoto)
Abstract : Networked control systems are of current research vitality due to their pervasive presence in many engineering areas and the
technical challenges they present in the control system design and analysis. In particular, there still remain difficulties in analyzing
stability of a networked control system when it involve finite level quantization in its feedback loop. Typical examples of the cause of
quantization in networked control systems include data rate constraints at communication channels and simple sensors/actuators which
can output only finite kinds of signals.
While, in many nonlinear systems, the well-known Small Gain Theorem has been successfully applied to provide a framework for stability
analysis, this is often difficult or even impossible to apply to networked control systems involving finite level quantization for
establishing stability. Observing this problem, we introduce a novel framework for stability analysis of networked control systems
involving quantization based on a new notion of small l-p signal l-p stability. Two illustrative examples are given to show the
usefulness of this framework: (i) robust stabilization of an uncertain plant over a rate-limited control channel and
(ii) stability analysis of networked control system affected by the combined effect of packet-dropouts and finite level quantization.
In the end of this talk, we also shortly discuss the possible extension of this framework to multi-agent systems.
[INMA] 2010-09-21 (15.00) : Optimization algorithms tailored for signal/image processing applications
At Euler building
Speaker :
Suviseshamuthu, Easter Selvan
Abstract :
The talk concerns a few applications of biologically-inspired/heuristic optimizers as well as a collection of deterministic optimization
algorithms on a manifold setting. First, it is demonstrated how a breast density model can be built, which serves as a breast cancer risk
assessment tool, by estimating the parameters of breast tissues'density distributions with the aid of heuristic optimizers; the segmentation
accuracy is shown to surpass the results obtained using the expectation maximization (EM) algorithm. Next, incorporating a trivial technique
for implicit imposition of the orthonormality constraint while optimizing an independence criterion with well-known global optimizers is
concisely presented; this application illustrates a way to handle a constraint in a more natural way with unconstrained optimizers suitable
for non-convex landscapes. Finally, a collection of deterministic optimizers on a trivial manifold is shown to improve the estimation accuracy of
unmixing matrix, as a consequence of relaxing the orthonormality constraint in the blind source separation problem.
[INMA] 2010-09-14 (14.00) : Refined instrumental variable methods for identification of Linear Parameter-Varying (LPV) Box-Jenkins models
At Euler building
Speaker :
Vincent LAURAIN (CRAN, Nancy-University, CNRS, France)
Abstract :
The common need for accurate and efficient control of today's industrial applications or modelling of natural systems is driving the system
identification field to face the constant challenge of providing better models of physical phenomena. This rises the need for system
descriptions that form an intermediate step between Linear Time-Invariant (LTI) systems and nonlinear/time-varying plants.
To cope with these expectations, the model class of Linear Parameter-Varying (LPV) systems provides an attractive candidate.
In LPV systems the signal relations are considered to be linear just as in the LTI case, but the parameters are assumed to be
functions of a measurable time-varying signal, the so-called scheduling variable. This LPV modelling concept allows for a wide
representation capability of physical processes but the real practical significance of the LPV framework until now lays in its
well worked out and industrially reputed control synthesis approaches that have led to many successful applications of LPV control
in practice. Despite the advances in the LPV control field, identification of such systems is not yet well developed. Most methods
assume static dependence on the scheduling parameters and it can be shown that it is generally impossible to reach statistically
optimal estimates by using linear regression as presented so far in the literature. Furthermore, there was a lack of an LPV identification
approach which is capable of efficiently estimating LPV-IO models underrealistic colored noise conditions.
Moreover, LPV identification methods are almost exclusively developed for DT as in this setting it is much easier to handle the estimation of
parameter-varying dynamics but there is a growing need of the LPV framework for efficient identification methods that directly deliver
reliable CT models.
In this seminar, a short horizon on available methods from the literature for identifying LPV IO models will be first given. An
instrumental variable method for identifying discrete-time LPV IO models under colored noise conditions will be presented along with its
extension to handle the direct identification of continuous-time LPV models.
[INMA] 2010-09-07 (14.00) : Bias in localization problems
At Euler building
Speaker :
Anderson, Brian D.O.
Abstract :
A common task in controlling formations or multi-robotic systems is the need to localize objects using various types of measurement, e.g. range, bearing,
time-difference-of-arrival, etc., These measurements are introduced into equations, which are generally nonlinear, whose solution yields the position
coordinates of the object. When the number of pieces of sensed data equals the ambient spatial dimension, the presence of any noise in the measurements,
coupled with the fact of the nonlinearity, will lead to a bias in the solution. When the dimension of the sensed data vector exceeds that of the ambient
space dimension, the equations themselves will generally be overdetermined, and they become insoluble in the presence of noise. Localization then usually
involves some kind of optimization problem, based on extremizing a maximum likelihood function. Again, bias is to be expected when the measurements are
contaminated by noise.
The seminar will discuss methods for computing the bias. What makes this problem unusually challenging is that the localization map, from sensed data
to estimated object position, is frequently not available in analytic form. What is available, normally in analytic form, is the mapping from object
position to noiseless sensed data. The technical issue is to establish how knowledge of this map can yield an expression for the bias in the localization
map.
Simulation data and data from flight trials demonstrate the improvement in localization quality that bias elimination can bring.
[INMA] 2010-06-22 (12:00) : Observers and filters with attitude: State estimation for kinematic systems on finite-dimensional connected Lie-groups
At Euler building
Speaker :
Mahony, R.
Abstract :
This talk considers the design of non-linear observers for invariant kinematic systems posed on finite-dimensional connected Lie groups,
and in particular on the group of rotations SO(3). Both the case of full-state measurements and partial-state measurements are considered
with the measurement process modelled by a transitive group action acting on a homogeneous manifold. Concepts of synchrony (between
observer and the plant) and invariant innovations are used to provide a structure theory for observer design. A detailed discussion of
the case of observers for attitude estimation based on state or vector measurements is provided.
Seminars
Subscribe (ICS)
INMA Contact Info
Mathematical Engineering (INMA)
L4.05.01
Avenue Georges Lemaître, 4
+32 10 47 80 36
secretaire-inma@uclouvain.be
Mon – Fri 9:00A.M. – 5:00P.M.
JOIN US