14 research outputs found
Generic Delay-L Left Invertibility of Structured Systems with Scalar Unknown Input
International audienc
Distributed Cyber-Attack Detection in the Secondary Control of DC Microgrids
The paper considers the problem of detecting
cyber-attacks occurring in communication networks typically
used in the secondary control layer of DC microgrids. The proposed
distributed methodology allows for scalable monitoring of
a microgrid and is able to detect the presence of data injection
attacks in the communications among Distributed Generation
Units (DGUs) - governed by consensus-based control - and
isolate the communication link over which the attack is injected.
Each local attack detector requires limited knowledge regarding
the dynamics of its neighbors. Detectability properties of the
method are analyzed, as well as a class of undetectable attacks.
Some results from numerical simulation are presented to
demonstrate the effectiveness of the proposed approach
CURRENT TRENDS AND CHALLENGES IN DISTRIBUTED CONTROL SYSTEMS – AN OVERVIEW
In this paper, innovations in the field of distributed control systems have been considered. Without any claim for completeness, a short summary on current trends in this area has been provided. A special attention is paid to application of blockchain technologies in distributed control systems, game theoretical approach for distributed control applications, and advantages of distributed control for power systems. Also, one of the main issues of modern distributed control systems – cybersecurity has been considered
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
Input and state estimation exploiting input sparsity
International audienc