1,853 research outputs found
Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey
Copyright © 2013 Jun Hu 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.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Consensus-based control for a network of diffusion PDEs with boundary local interaction
In this paper the problem of driving the state of a network of identical
agents, modeled by boundary-controlled heat equations, towards a common
steady-state profile is addressed. Decentralized consensus protocols are
proposed to address two distinct problems. The first problem is that of
steering the states of all agents towards the same constant steady-state
profile which corresponds to the spatial average of the agents initial
condition. A linear local interaction rule addressing this requirement is
given. The second problem deals with the case where the controlled boundaries
of the agents dynamics are corrupted by additive persistent disturbances. To
achieve synchronization between agents, while completely rejecting the effect
of the boundary disturbances, a nonlinear sliding-mode based consensus protocol
is proposed. Performance of the proposed local interaction rules are analyzed
by applying a Lyapunov-based approach. Simulation results are presented to
support the effectiveness of the proposed algorithms
Multiplex PI-Control for Consensus in Networks of Heterogeneous Linear Agents
In this paper, we propose a multiplex proportional-integral approach, for
solving consensus problems in networks of heterogeneous nodes dynamics affected
by constant disturbances. The proportional and integral actions are deployed on
two different layers across the network, each with its own topology. Sufficient
conditions for convergence are derived that depend upon the structure of the
network, the parameters characterizing the control layers and the node
dynamics. The effectiveness of the theoretical results is illustrated using a
power network model as a representative example.Comment: 13 pages, 6 Figures, Preprint submitted to Automatic
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack
In this paper, we show synchronization for a group of output passive agents
that communicate with each other according to an underlying communication graph
to achieve a common goal. We propose a distributed event-triggered control
framework that will guarantee synchronization and considerably decrease the
required communication load on the band-limited network. We define a general
Byzantine attack on the event-triggered multi-agent network system and
characterize its negative effects on synchronization. The Byzantine agents are
capable of intelligently falsifying their data and manipulating the underlying
communication graph by altering their respective control feedback weights. We
introduce a decentralized detection framework and analyze its steady-state and
transient performances. We propose a way of identifying individual Byzantine
neighbors and a learning-based method of estimating the attack parameters.
Lastly, we propose learning-based control approaches to mitigate the negative
effects of the adversarial attack
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