129 research outputs found
Learning Equations for Extrapolation and Control
We present an approach to identify concise equations from data using a
shallow neural network approach. In contrast to ordinary black-box regression,
this approach allows understanding functional relations and generalizing them
from observed data to unseen parts of the parameter space. We show how to
extend the class of learnable equations for a recently proposed equation
learning network to include divisions, and we improve the learning and model
selection strategy to be useful for challenging real-world data. For systems
governed by analytical expressions, our method can in many cases identify the
true underlying equation and extrapolate to unseen domains. We demonstrate its
effectiveness by experiments on a cart-pendulum system, where only 2 random
rollouts are required to learn the forward dynamics and successfully achieve
the swing-up task.Comment: 9 pages, 9 figures, ICML 201
Digital Twins for Moving Target Defense Validation in AC Microgrids
Cyber-physical microgrids are vulnerable to stealth attacks that can degrade
their stability and operability by performing low-magnitude manipulations in a
coordinated manner. This paper formulates the interactions between CSAs and
microgrid defenders as a non-cooperative, zero-sum game. Additionally, it
presents a hybrid Moving Target Defense (MTD) strategy for distributed
microgrids that can dynamically alter local control gains to achieve resiliency
against Coordinated Stealth Attacks (CSAs). The proposed strategy reduces the
success probability of attack(s) by making system dynamics less predictable.
The framework also identifies and removes malicious injections by modifying
secondary control weights assigned to them. The manipulated signals are
reconstructed using an Artificial Neural Network (ANN)-based Digital Twin (DT)
to preserve stability. To guarantee additional immunity against instability
arising from gain alterations, MTD decisions are also validated (via utility
and best response computations) using the DT before actual implementation. The
DT is also used to find the minimum perturbation that defenders must achieve to
invalidate an attacker's knowledge effectively.Comment: IEEE Energy Conversion Congress and Expo (ECCE) 202
Stabilization of DC Microgrids Under Stealth Cyber Attacks - Optimal Design and Sensitivity Analysis
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