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Found 13 papers, 0 papers with code
Uncertainty Learning for LTI Systems with Stability Guarantees
We propose a methodology to extend the dynamics of an LTI (without uncertainty) with an uncertainty model, based on measured data, to improve the predictive capacity of the model in the input-output sense.
Attack-Resilient Design for Connected and Automated Vehicles
Given enough system knowledge, adversaries are still able to launch a range of attacks that can surpass the detection scheme by hiding within the system disturbances and uncertainty -- we refer to this class of attacks as \textit{stealthy FDI attacks}.
Robust Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design
We provide sufficient conditions that guarantee stability of the estimation error dynamics: firstly, asymptotic stability (i. e., perfect fault estimation) in the absence of perturbations induced by fault model mismatch (mismatch between internal, ultralocal model for the fault and the actual fault characteristics), uncertainty, external disturbances, and measurement noise and, secondly, Input-to-State Stability (ISS) of the estimation error dynamics is guaranteed in the presence of these perturbations.
Impact Sensitivity Analysis of Cooperative Adaptive Cruise Control Against Resource-Limited Adversaries
We use the size of these sets as a security metric to quantify the potential damage of attacks affecting different signals in a CACC-controlled vehicle and study how two key system parameters change this metric.
Plug-and-Play Secondary Control for Safety of LTI Systems under Attacks
To further ensure the safety of the states of the system, we choose a subset of sensors that can be locally secured and made free of attacks.
Linear Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design
The proposed method augments the system dynamics with an approximated internal linear model of the combined contribution of known nonlinearities and unknown faults -- leading to an approximated linear model in the augmented state.
Privacy-Preserving Anomaly Detection in Stochastic Dynamical Systems: Synthesis of Optimal Gaussian Mechanisms
We present a framework for designing distorting mechanisms that allow remotely operating anomaly detectors while preserving privacy.
Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption
The idea is to immerse the learning algorithm, a Stochastic Gradient Decent (SGD), into a higher-dimensional system (the so-called target system) and design the dynamics of the target system so that: the trajectories of the original SGD are immersed/embedded in its trajectories, and it learns on encrypted data (here we use random matrix encryption).
Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction
In this paper, we present a methodology for actuator and sensor fault estimation in nonlinear systems.
Gaussian Mechanisms Against Statistical Inference: Synthesis Tools
We formulate the synthesis of distorting mechanisms in terms of semidefinite programs in which we seek to minimize the mutual information (our privacy metric) between private data and the disclosed distorted data given a desired distortion level -- how different actual and distorted data are allowed to be.
Risk Assessment for Connected Vehicles under Stealthy Attacks on Vehicle-to-Vehicle Networks
In this manuscript, we propose a novel attack detection scheme that leverage real-time sensor/network data and physics-based mathematical models of vehicles in the platoon.
A Robust CACC Scheme Against Cyberattacks Via Multiple Vehicle-to-Vehicle Networks
The idea is to transmit acceleration commands multiple times through different communication networks (channels) to create redundancy at the receiver side.
Learning Agents in Black-Scholes Financial Markets: Consensus Dynamics and Volatility Smiles
Black-Scholes (BS) is the standard mathematical model for option pricing in financial markets.
