38 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 Satisfiability Modulo Theory Approach to Secure State Reconstruction in Differentially Flat Systems Under Sensor Attacks
We address the problem of estimating the state of a differentially flat
system from measurements that may be corrupted by an adversarial attack. In
cyber-physical systems, malicious attacks can directly compromise the system's
sensors or manipulate the communication between sensors and controllers. We
consider attacks that only corrupt a subset of sensor measurements. We show
that the possibility of reconstructing the state under such attacks is
characterized by a suitable generalization of the notion of s-sparse
observability, previously introduced by some of the authors in the linear case.
We also extend our previous work on the use of Satisfiability Modulo Theory
solvers to estimate the state under sensor attacks to the context of
differentially flat systems. The effectiveness of our approach is illustrated
on the problem of controlling a quadrotor under sensor attacks.Comment: arXiv admin note: text overlap with arXiv:1412.432
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
Detection of Sensor Attack and Resilient State Estimation for Uniformly Observable Nonlinear Systems having Redundant Sensors
This paper presents a detection algorithm for sensor attacks and a resilient
state estimation scheme for a class of uniformly observable nonlinear systems.
An adversary is supposed to corrupt a subset of sensors with the possibly
unbounded signals, while the system has sensor redundancy. We design an
individual high-gain observer for each measurement output so that only the
observable portion of the system state is obtained. Then, a nonlinear error
correcting problem is solved by collecting all the information from those
partial observers and exploiting redundancy. A computationally efficient,
on-line monitoring scheme is presented for attack detection. Based on the
attack detection scheme, an algorithm for resilient state estimation is
provided. The simulation results demonstrate the effectiveness of the proposed
algorithm