78,188 research outputs found
Robot Impedance Control and Passivity Analysis with Inner Torque and Velocity Feedback Loops
Impedance control is a well-established technique to control interaction
forces in robotics. However, real implementations of impedance control with an
inner loop may suffer from several limitations. Although common practice in
designing nested control systems is to maximize the bandwidth of the inner loop
to improve tracking performance, it may not be the most suitable approach when
a certain range of impedance parameters has to be rendered. In particular, it
turns out that the viable range of stable stiffness and damping values can be
strongly affected by the bandwidth of the inner control loops (e.g. a torque
loop) as well as by the filtering and sampling frequency. This paper provides
an extensive analysis on how these aspects influence the stability region of
impedance parameters as well as the passivity of the system. This will be
supported by both simulations and experimental data. Moreover, a methodology
for designing joint impedance controllers based on an inner torque loop and a
positive velocity feedback loop will be presented. The goal of the velocity
feedback is to increase (given the constraints to preserve stability) the
bandwidth of the torque loop without the need of a complex controller.Comment: 14 pages in Control Theory and Technology (2016
Cluster-based reduced-order modelling of a mixing layer
We propose a novel cluster-based reduced-order modelling (CROM) strategy of
unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger's
group (Burkardt et al. 2006) and and transition matrix models introduced in
fluid dynamics in Eckhardt's group (Schneider et al. 2007). CROM constitutes a
potential alternative to POD models and generalises the Ulam-Galerkin method
classically used in dynamical systems to determine a finite-rank approximation
of the Perron-Frobenius operator. The proposed strategy processes a
time-resolved sequence of flow snapshots in two steps. First, the snapshot data
are clustered into a small number of representative states, called centroids,
in the state space. These centroids partition the state space in complementary
non-overlapping regions (centroidal Voronoi cells). Departing from the standard
algorithm, the probabilities of the clusters are determined, and the states are
sorted by analysis of the transition matrix. Secondly, the transitions between
the states are dynamically modelled using a Markov process. Physical mechanisms
are then distilled by a refined analysis of the Markov process, e.g. using
finite-time Lyapunov exponent and entropic methods. This CROM framework is
applied to the Lorenz attractor (as illustrative example), to velocity fields
of the spatially evolving incompressible mixing layer and the three-dimensional
turbulent wake of a bluff body. For these examples, CROM is shown to identify
non-trivial quasi-attractors and transition processes in an unsupervised
manner. CROM has numerous potential applications for the systematic
identification of physical mechanisms of complex dynamics, for comparison of
flow evolution models, for the identification of precursors to desirable and
undesirable events, and for flow control applications exploiting nonlinear
actuation dynamics.Comment: 48 pages, 30 figures. Revised version with additional material.
Accepted for publication in Journal of Fluid Mechanic
Efficient Algorithms for the Consensus Decision Problem
We address the problem of determining if a discrete time switched consensus
system converges for any switching sequence and that of determining if it
converges for at least one switching sequence. For these two problems, we
provide necessary and sufficient conditions that can be checked in singly
exponential time. As a side result, we prove the existence of a polynomial time
algorithm for the first problem when the system switches between only two
subsystems whose corresponding graphs are undirected, a problem that had been
suggested to be NP-hard by Blondel and Olshevsky.Comment: Small modifications after comments from reviewer
Stability implications of delay distribution for first-order and second-order systems
Kiss, G., & Krauskopf, B. (2009). Stability implications of delay distribution for first-order and second-order systems. Early version, also known as pre-print Link to publication record in Explore Bristol Research PDF-documen
Optimising Spatial and Tonal Data for PDE-based Inpainting
Some recent methods for lossy signal and image compression store only a few
selected pixels and fill in the missing structures by inpainting with a partial
differential equation (PDE). Suitable operators include the Laplacian, the
biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The
quality of such approaches depends substantially on the selection of the data
that is kept. Optimising this data in the domain and codomain gives rise to
challenging mathematical problems that shall be addressed in our work.
In the 1D case, we prove results that provide insights into the difficulty of
this problem, and we give evidence that a splitting into spatial and tonal
(i.e. function value) optimisation does hardly deteriorate the results. In the
2D setting, we present generic algorithms that achieve a high reconstruction
quality even if the specified data is very sparse. To optimise the spatial
data, we use a probabilistic sparsification, followed by a nonlocal pixel
exchange that avoids getting trapped in bad local optima. After this spatial
optimisation we perform a tonal optimisation that modifies the function values
in order to reduce the global reconstruction error. For homogeneous diffusion
inpainting, this comes down to a least squares problem for which we prove that
it has a unique solution. We demonstrate that it can be found efficiently with
a gradient descent approach that is accelerated with fast explicit diffusion
(FED) cycles. Our framework allows to specify the desired density of the
inpainting mask a priori. Moreover, is more generic than other data
optimisation approaches for the sparse inpainting problem, since it can also be
extended to nonlinear inpainting operators such as EED. This is exploited to
achieve reconstructions with state-of-the-art quality.
We also give an extensive literature survey on PDE-based image compression
methods
Development of numerical algorithms for practical computation of nonlinear normal modes
When resorting to numerical algorithms, we show that nonlinear normal mode (NNM) computation is possible
with limited implementation effort, which paves the way to a practical method for determining the NNMs
of nonlinear mechanical systems. The proposed method relies on two main techniques, namely a shooting
procedure and a method for the continuation of NNM motions. In addition, sensitivity analysis is used to
reduce the computational burden of the algorithm. A simplified discrete model of a nonlinear bladed disk is
considered to demonstrate the developments
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