50,747 research outputs found
Time-and event-driven communication process for networked control systems: A survey
Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Consensus in multi-agent systems with second-order dynamics and non-periodic sampled-data exchange
In this paper consensus in second-order multi-agent systems with a
non-periodic sampled-data exchange among agents is investigated. The sampling
is random with bounded inter-sampling intervals. It is assumed that each agent
has exact knowledge of its own state at all times. The considered local
interaction rule is PD-type. The characterization of the convergence properties
exploits a Lyapunov-Krasovskii functional method, sufficient conditions for
stability of the consensus protocol to a time-invariant value are derived.
Numerical simulations are presented to corroborate the theoretical results.Comment: The 19th IEEE International Conference on Emerging Technologies and
Factory Automation (ETFA'2014), Barcelona (Spain
Covariant Lyapunov vectors
The recent years have witnessed a growing interest for covariant Lyapunov
vectors (CLVs) which span local intrinsic directions in the phase space of
chaotic systems. Here we review the basic results of ergodic theory, with a
specific reference to the implications of Oseledets' theorem for the properties
of the CLVs. We then present a detailed description of a "dynamical" algorithm
to compute the CLVs and show that it generically converges exponentially in
time. We also discuss its numerical performance and compare it with other
algorithms presented in literature. We finally illustrate how CLVs can be used
to quantify deviations from hyperbolicity with reference to a dissipative
system (a chain of H\'enon maps) and a Hamiltonian model (a Fermi-Pasta-Ulam
chain)
A looped-functional approach for robust stability analysis of linear impulsive systems
A new functional-based approach is developed for the stability analysis of
linear impulsive systems. The new method, which introduces looped-functionals,
considers non-monotonic Lyapunov functions and leads to LMIs conditions devoid
of exponential terms. This allows one to easily formulate dwell-times results,
for both certain and uncertain systems. It is also shown that this approach may
be applied to a wider class of impulsive systems than existing methods. Some
examples, notably on sampled-data systems, illustrate the efficiency of the
approach.Comment: 13 pages, 2 figures, Accepted at Systems & Control Letter
Consensus in multi-agent systems with non-periodic sampled-data exchange and uncertain network topology
In this paper consensus in second-order multi-agent systems with a
non-periodic sampled-data exchange among agents is investigated. The sampling
is random with bounded inter-sampling intervals. It is assumed that each agent
has exact knowledge of its own state at any time instant. The considered local
interaction rule is PD-type. Sufficient conditions for stability of the
consensus protocol to a time-invariant value are derived based on LMIs. Such
conditions only require the knowledge of the connectivity of the graph modeling
the network topology. Numerical simulations are presented to corroborate the
theoretical results.Comment: arXiv admin note: substantial text overlap with arXiv:1407.300
Complex Dynamics and Synchronization of Delayed-Feedback Nonlinear Oscillators
We describe a flexible and modular delayed-feedback nonlinear oscillator that
is capable of generating a wide range of dynamical behaviours, from periodic
oscillations to high-dimensional chaos. The oscillator uses electrooptic
modulation and fibre-optic transmission, with feedback and filtering
implemented through real-time digital-signal processing. We consider two such
oscillators that are coupled to one another, and we identify the conditions
under which they will synchronize. By examining the rates of divergence or
convergence between two coupled oscillators, we quantify the maximum Lyapunov
exponents or transverse Lyapunov exponents of the system, and we present an
experimental method to determine these rates that does not require a
mathematical model of the system. Finally, we demonstrate a new adaptive
control method that keeps two oscillators synchronized even when the coupling
between them is changing unpredictably.Comment: 24 pages, 13 figures. To appear in Phil. Trans. R. Soc. A (special
theme issue to accompany 2009 International Workshop on Delayed Complex
Systems
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