7,442 research outputs found
Static consensus in passifiable linear networks
Sufficient conditions of consensus (synchronization) in networks described by
digraphs and consisting of identical determenistic SIMO systems are derived.
Identical and nonidentical control gains (positive arc weights) are considered.
Connection between admissible digraphs and nonsmooth hypersurfaces (sufficient
gain boundary) is established. Necessary and sufficient conditions for static
consensus by output feedback in networks consisting of certain class of double
integrators are rediscovered. Scalability for circle digraph in terms of gain
magnitudes is studied. Examples and results of numerical simulations are
presented.Comment: 13 pages, 5 figure
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
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