225 research outputs found
New summation inequalities and their applications to discrete-time delay systems
This paper provides new summation inequalities in both single and double
forms to be used in stability analysis of discrete-time systems with
time-varying delays. The potential capability of the newly derived inequalities
is demonstrated by establishing less conservative stability conditions for a
class of linear discrete-time systems with an interval time-varying delay in
the framework of linear matrix inequalities. The effectiveness and least
conservativeness of the derived stability conditions are shown by academic and
practical examples.Comment: 15 pages, 01 figur
Further results on exponential estimates of markovian jump systems with mode-dependent time-varying delays
This technical note studies the problem of exponential estimates for Markovian jump systems with mode-dependent interval time-varying delays. A novel LyapunovKrasovskii functional (LKF) is constructed with the idea of delay partitioning, and a less conservative exponential estimate criterion is obtained based on the new LKF. Illustrative examples are provided to show the effectiveness of the proposed results. Ā© 2010 IEEE.published_or_final_versio
Robust H
A new version of delay-dependent bounded real lemma for singular systems with state delay is established by parameterized Lyapunov-Krasovskii functional approach. In order to avoid generating nonconvex problem formulations in control design, a strategy that introduces slack matrices and decouples the system matrices from the Lyapunov-Krasovskii parameter matrices is used. Examples are provided to demonstrate that the results in this paper are less conservative than the existing corresponding ones in the literature
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Robust filtering for stochastic genetic regulatory networks with time-varying delay
This is the post print version of the article. The official published version can be obtained from the link - Copyright 2009 Elsevier LtdThis paper addresses the robust filtering problem for a class of linear genetic regulatory networks (GRNs) with stochastic disturbances, parameter uncertainties and time delays. The parameter uncertainties are assumed to reside in a polytopic region, the stochastic disturbance is state-dependent described by a scalar Brownian motion, and the time-varying delays enter into both the translation process and the feedback regulation process. We aim to estimate the true concentrations of mRNA and protein by designing a linear filter such that, for all admissible time delays, stochastic disturbances as well as polytopic uncertainties, the augmented state estimation dynamics is exponentially mean square stable with an expected decay rate. A delay-dependent linear matrix inequality (LMI) approach is first developed to derive sufficient conditions that guarantee the exponential stability of the augmented dynamics, and then the filter gains are parameterized in terms of the solution to a set of LMIs. Note that LMIs can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the U.K. under Grants BB/C506264/1 and 100/EGM17735, an International Joint Project sponsored by the Royal Society of the U.K., the Research Grants Council of Hong Kong under Grant HKU 7031/06P, the National Natural Science Foundation of China under Grant 60804028, and the Alexander von Humboldt Foundation of Germany
Adaptive Shape Servoing of Elastic Rods using Parameterized Regression Features and Auto-Tuning Motion Controls
In this paper, we present a new vision-based method to control the shape of
elastic rods with robot manipulators. Our new method computes parameterized
regression features from online sensor measurements that enable to
automatically quantify the object's configuration and establish an explicit
shape servo-loop. To automatically deform the rod into a desired shape, our
adaptive controller iteratively estimates the differential transformation
between the robot's motion and the relative shape changes; This valuable
capability allows to effectively manipulate objects with unknown mechanical
models. An auto-tuning algorithm is introduced to adjust the robot's shaping
motion in real-time based on optimal performance criteria. To validate the
proposed theory, we present a detailed numerical and experimental study with
vision-guided robotic manipulators.Comment: 13 pages, 22 figures, 2 table
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