376 research outputs found
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
Data-driven Safe Control of Linear Systems Under Epistemic and Aleatory Uncertainties
Safe control of constrained linear systems under both epistemic and aleatory
uncertainties is considered. The aleatory uncertainty characterizes random
noises and is modeled by a probability distribution function (PDF) and the
epistemic uncertainty characterizes the lack of knowledge on the system
dynamics. Data-based probabilistic safe controllers are designed for the cases
where the noise PDF is 1) zero-mean Gaussian with a known covariance, 2)
zero-mean Gaussian with an uncertain covariance, and 3) zero-mean non-Gaussian
with an unknown distribution. Easy-to-check model-based conditions for
guaranteeing probabilistic safety are provided for the first case by
introducing probabilistic contractive sets. These results are then extended to
the second and third cases by leveraging distributionally-robust probabilistic
safe control and conditional value-at-risk (CVaR) based probabilistic safe
control, respectively. Data-based implementations of these probabilistic safe
controllers are then considered. It is shown that data-richness requirements
for directly learning a safe controller is considerably weaker than
data-richness requirements for model-based safe control approaches that
undertake a model identification. Moreover, an upper bound on the minimal risk
level, under which the existence of a safe controller is guaranteed, is learned
using collected data. A simulation example is provided to show the
effectiveness of the proposed approach
آگاهی دانشجویان پزشکی و دندانپزشکی دانشگاه علوم پزشکی کرمان درباره ی عفونت و واکسیناسیون ویروس پاپیلومای انسانی
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