All Years Seminars
[INMA] 2008-02-05 (14.00) : Computability in Linear Dynamical Systems
At Euler building
Speaker :
Paul BELL
(University of Liverpool/CESAME )
Abstract :
We shall present some current results of computational problems on
matrix semigroups and low dimensional dynamical systems with an
emphasis on reachability and recurrence type problems. In such
problems we are given a finite set of matrices over a semiring and
wish to decide whether certain properties of the semigroup generated
by this set are algorithmically decidable or not. These problems
very often have interpretations in terms of linear dynamical systems
and we shall highlight the connection between these two subjects. We
shall also see some matrix interpretations of Skolem's problem
concerning linear recurrent sequences. Open problems in these areas
and possibilities for future work will also be presented.
[INMA] 2008-01-29 (14.00) : A Riemannian Framework for the Regularization of Symmetric Positive-Definite Tensor Fields
At Euler building
Speaker :
Maher MOAKHER
( Ecole Nationale d'Ingenieurs de Tunis
)
Abstract :
In this talk we present a Riemannian framework for the smoothing of data that are constrained to live in the space of symmetric
positive-definite matrices. We start by giving the differential geometry of this space. We then use the harmonic map and minimal
immersion theories to construct three flows that drive a noisy field of symmetric positive-definite data into a smooth one.
The harmonic map flow is the equivalent of the heat flow or isotropic linear diffusion which smooths data everywhere.
A modification of the harmonic flow leads to a Perona-Malik like flow which is a selective smoother that preserves edges.
The minimal immersion flow gives rise to a nonlinear system of coupled diffusion equations with anisotropic diffusivity.
Some preliminary numerical results are presented for synthetic diffusion tensor MRI data.
[INMA] 2008-01-08 (14.00) : An Introduction to Dynamic Encoding
At Euler building
Speaker :
Ignasi COS-AGUILERA
(HWNI, UC Berkeley, USA)
Abstract :
The Brain Machine Interface paradigm relies on cortical adaptivity to
generate and control movement of an external device. In particular,
previous experiments have demonstrated the validity of motor brain areas
for controlling a cursor on a computer screen with the help of visual
feedback. However, these approaches lack control of every parameter
involved in the motion of a physical object, the parameters controlling
the dynamics of the movement: inertia, stiffness and viscosity.
In a complementary fashion to this main stream, we argue that decoding of those parameters from the cortex is the most natural
procedure to control a prosthetic device. Eventually, dynamic control should be the one requiring the least effort to adapt to.
From this perspective, we have been studying the procedures of control of movement within force-field conditions via psychophysical
experiments, aiming at understanding the adaptation process of the brain to situations requiring accountancy of the dynamics of motion.
The experiments demonstrate different strategies to cope with different force-field conditions, suggesting that once adaptation is complete,
movement is controlled via modulation of impedance. Stiffness, viscosity and intertia are optimized according to the position and type of reach.
We present some of these experiments together with a prospect of future research.
[INMA] 2007-12-14 (15.00) : Thèse de doctorat, "Tracking the invisible requires prediction and internal models"
At Barb 94
Speaker :
Orban de Xivry, Jean-Jacques
Abstract :
[INMA] 2007-12-11 (14.00) : "Fast implementation of MPC using Set Membership aproximation with application to power generating kites"
At Euler building
Speaker :
Lorenzo FAGIANO
(Politecnico di Torino, Italy)
Abstract :
Fast implementation of model predictive control is presented using Set Membership (SM) methodologies for nonlinear function estimation. A key feature of this approach is the
possibility of tuning the approximation error in order to guarantee
stability, satisfaction of state constraints and performances. This way, as the control computation
is simply reduced to the
evaluation of a static nonlinear approximating function, the computational time is
significantly reduced leading to a Fast Model
Predictive Control implementation (FMPC). Two different SM methodologies
are presented for the approximation
of the nominal MPC controller. The first method derives an optimal
approximating function which minimizes the guaranteed accuracy level. The second one gives lower guaranteed accuracy but also lower computational time. Numerical examples are given to show
the effectiveness of the presented results. Finally, application of the presented technique to the problem of control of power kites for electric energy generation is presented.
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