732 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
A system-theoretic framework for privacy preservation in continuous-time multiagent dynamics
In multiagent dynamical systems, privacy protection corresponds to avoid
disclosing the initial states of the agents while accomplishing a distributed
task. The system-theoretic framework described in this paper for this scope,
denoted dynamical privacy, relies on introducing output maps which act as
masks, rendering the internal states of an agent indiscernible by the other
agents as well as by external agents monitoring all communications. Our output
masks are local (i.e., decided independently by each agent), time-varying
functions asymptotically converging to the true states. The resulting masked
system is also time-varying, and has the original unmasked system as its limit
system. When the unmasked system has a globally exponentially stable
equilibrium point, it is shown in the paper that the masked system has the same
point as a global attractor. It is also shown that existence of equilibrium
points in the masked system is not compatible with dynamical privacy.
Application of dynamical privacy to popular examples of multiagent dynamics,
such as models of social opinions, average consensus and synchronization, is
investigated in detail.Comment: 38 pages, 4 figures, extended version of arXiv preprint
arXiv:1808.0808
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally,
conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002
and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140
Distributed Event-triggered Fault-tolerant Consensus Control of Multi-agent Systems under DoS Attacks
This study investigates the distributed fault-tolerant consensus issue of multi-agent systems subject to complicated abrupt and incipient time-varying actuator faults in physical hierarchy and aperiodic denial-of-service (DoS) attacks in networked hierarchy. Decentralized estimators are devised to estimate consecutive system states and actuator faults. A unified framework with an absolute local output-based closed-loop estimator in decentralized fault estimation design and a relative broadcasting state-based open-loop estimator in distributed event-triggered fault-tolerant consensus design is developed. Criteria of exponential consensus of the faulty multi-agent systems under DoS attacks are derived by virtue of average dwelling time and attack frequency technique. Simulations are outlined to confirm the efficacy of the proposed distributed fault-tolerant consensus control algorithm based on an event-triggered mechanism
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