1,925 research outputs found
An Unknown Input Multi-Observer Approach for Estimation and Control under Adversarial Attacks
We address the problem of state estimation, attack isolation, and control of
discrete-time linear time-invariant systems under (potentially unbounded)
actuator and sensor false data injection attacks. Using a bank of unknown input
observers, each observer leading to an exponentially stable estimation error
(in the attack-free case), we propose an observer-based estimator that provides
exponential estimates of the system state in spite of actuator and sensor
attacks. Exploiting sensor and actuator redundancy, the estimation scheme is
guaranteed to work if a sufficiently small subset of sensors and actuators are
under attack. Using the proposed estimator, we provide tools for reconstructing
and isolating actuator and sensor attacks; and a control scheme capable of
stabilizing the closed-loop dynamics by switching off isolated actuators.
Simulation results are presented to illustrate the performance of our tools.Comment: arXiv admin note: substantial text overlap with arXiv:1811.1015
An Unknown Input Multi-Observer Approach for Estimation, Attack Isolation, and Control of LTI Systems under Actuator Attacks
We address the problem of state estimation, attack isolation, and control for
discrete-time Linear Time Invariant (LTI) systems under (potentially unbounded)
actuator false data injection attacks. Using a bank of Unknown Input Observers
(UIOs), each observer leading to an exponentially stable estimation error in
the attack-free case, we propose an estimator that provides exponential
estimates of the system state and the attack signals when a sufficiently small
number of actuators are attacked. We use these estimates to control the system
and isolate actuator attacks. Simulations results are presented to illustrate
the performance of the results
A Multi-Observer Based Estimation Framework for Nonlinear Systems under Sensor Attacks
We address the problem of state estimation and attack isolation for general
discrete-time nonlinear systems when sensors are corrupted by (potentially
unbounded) attack signals. For a large class of nonlinear plants and observers,
we provide a general estimation scheme, built around the idea of sensor
redundancy and multi-observer, capable of reconstructing the system state in
spite of sensor attacks and noise. This scheme has been proposed by others for
linear systems/observers and here we propose a unifying framework for a much
larger class of nonlinear systems/observers. Using the proposed estimator, we
provide an isolation algorithm to pinpoint attacks on sensors during sliding
time windows. Simulation results are presented to illustrate the performance of
our tools.Comment: arXiv admin note: text overlap with arXiv:1806.0648
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
On Reachable Sets of Hidden CPS Sensor Attacks
For given system dynamics, observer structure, and observer-based
fault/attack detection procedure, we provide mathematical tools -- in terms of
Linear Matrix Inequalities (LMIs) -- for computing outer ellipsoidal bounds on
the set of estimation errors that attacks can induce while maintaining the
alarm rate of the detector equal to its attack-free false alarm rate. We refer
to these sets to as hidden reachable sets. The obtained ellipsoidal bounds on
hidden reachable sets quantify the attacker's potential impact when it is
constrained to stay hidden from the detector. We provide tools for minimizing
the volume of these ellipsoidal bounds (minimizing thus the reachable sets) by
redesigning the observer gains. Simulation results are presented to illustrate
the performance of our tools
On Joint Reconstruction of State and Input-Output Injection Attacks for Nonlinear Systems
We address the problem of robust state reconstruction for discrete-time
nonlinear systems when the actuators and sensors are injected with (potentially
unbounded) attack signals. Exploiting redundancy in sensors and actuators and
using a bank of unknown input observers (UIOs), we propose an observer-based
estimator capable of providing asymptotic estimates of the system state and
attack signals under the condition that the numbers of sensors and actuators
under attack are sufficiently small. Using the proposed estimator, we provide
methods for isolating the compromised actuators and sensors. Numerical examples
are provided to demonstrate the effectiveness of our methods.Comment: arXiv admin note: text overlap with arXiv:1904.0423
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