activity
20192025
most citedLearning to Control Linear Systems can be Hard

3 citations · 7 across the 9 of their papers we have counts for

collaborators
Showing eess.SYShow all

6 papers · 1 filter

eess.SY2024

Online Residual Learning from Offline Experts for Pedestrian Tracking

Anastasios Vlachos, Anastasios Tsiamis, Aren Karapetyan +2

In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-tra…

eess.SY20221 cited

Secure state estimation over Markov wireless communication channels (extended version)

Anastasia Impicciatore, Anastasios Tsiamis, Yuriy Zacchia Lun +2

This note studies state estimation in wireless networked control systems with secrecy against eavesdropping. Specifically, a sensor transmits a system state information to the esti…

eess.SY20221 cited

Adaptive Stochastic MPC under Unknown Noise Distribution

Charis Stamouli, Anastasios Tsiamis, Manfred Morari +1

In this paper, we address the stochastic MPC (SMPC) problem for linear systems, subject to chance state constraints and hard input constraints, under unknown noise distribution. Fi…

eess.SY2021

Linear Systems can be Hard to Learn

Anastasios Tsiamis, George J. Pappas

In this paper, we investigate when system identification is statistically easy or hard, in the finite sample regime. Statistically easy to learn linear system classes have sample c…

eess.SY2020

Risk-Constrained Linear-Quadratic Regulators

Anastasios Tsiamis, Dionysios S. Kalogerias, Luiz F. O. Chamon +2

We propose a new risk-constrained reformulation of the standard Linear Quadratic Regulator (LQR) problem. Our framework is motivated by the fact that the classical (risk-neutral) L…

eess.SY2019

Sample Complexity of Kalman Filtering for Unknown Systems

Anastasios Tsiamis, Nikolai Matni, George J. Pappas

In this paper, we consider the task of designing a Kalman Filter (KF) for an unknown and partially observed autonomous linear time invariant system driven by process and sensor noi…