2 citations · 4 across the 11 of their papers we have counts for
12 papers
Beyond Quadratic Costs: A Bregman Divergence Approach to H Control
Joudi Hajar, Reza Ghane, Babak Hassibi
In the past couple of decades, non-quadratic convex penalties have reshaped signal processing and machine learning; in robust control, however, general convex costs break the Ricca…
Beyond Quadratic Costs in LQR: Bregman Divergence Control
Babak Hassibi, Joudi Hajar, Reza Ghane
In the past couple of decades, the use of ``non-quadratic" convex cost functions has revolutionized signal processing, machine learning, and statistics, allowing one to customize s…
Optimal Infinite-Horizon Mixed Control
Vikrant Malik, Taylan Kargin, Joudi Hajar +1
We study the problem of mixed control in the infinite-horizon setting. We identify the optimal causal controller that minimizes the …
The Distributionally Robust Infinite-Horizon LQR
Joudi Hajar, Taylan Kargin, Vikrant Malik +1
We explore the infinite-horizon Distributionally Robust (DR) linear-quadratic control. While the probability distribution of disturbances is unknown and potentially correlated over…
Distributionally Robust Kalman Filtering over Finite and Infinite Horizon
Taylan Kargin, Joudi Hajar, Vikrant Malik +1
This paper investigates the distributionally robust filtering of signals generated by state-space models driven by exogenous disturbances with noisy observations in finite and infi…
Regret-Optimal Defense Against Stealthy Adversaries: A System Level Approach
Hiroyasu Tsukamoto, Joudi Hajar, Soon-Jo Chung +1
Modern control designs in robotics, aerospace, and cyber-physical systems rely heavily on real-world data obtained through system outputs. However, these outputs can be compromised…
Robust Optimal Network Topology Switching for Zero Dynamics Attacks
Hiroyasu Tsukamoto, Joshua D. Ibrahim, Joudi Hajar +3
The intrinsic, sampling, and enforced zero dynamics attacks (ZDAs) are among the most detrimental stealthy attacks in robotics, aerospace, and cyber-physical systems. They exploit…
Infinite-Horizon Distributionally Robust Regret-Optimal Control
Taylan Kargin, Joudi Hajar, Vikrant Malik +1
We study the infinite-horizon distributionally robust (DR) control of linear systems with quadratic costs, where disturbances have unknown, possibly time-correlated distribution wi…
Wasserstein Distributionally Robust Regret-Optimal Control in the Infinite-Horizon
Taylan Kargin, Joudi Hajar, Vikrant Malik +1
We investigate the Distributionally Robust Regret-Optimal (DR-RO) control of discrete-time linear dynamical systems with quadratic cost over an infinite horizon. Regret is the diff…
Regret-Optimal Control under Partial Observability
Joudi Hajar, Oron Sabag, Babak Hassibi
This paper studies online solutions for regret-optimal control in partially observable systems over an infinite-horizon. Regret-optimal control aims to minimize the difference in L…
Wasserstein Distributionally Robust Regret-Optimal Control under Partial Observability
Joudi Hajar, Taylan Kargin, Babak Hassibi
This paper presents a framework for Wasserstein distributionally robust (DR) regret-optimal (RO) control in the context of partially observable systems. DR-RO control considers the…
Modeling of Political Systems using Wasserstein Gradient Flows
Nicolas Lanzetti, Joudi Hajar, Florian Dörfler
The study of complex political phenomena such as parties' polarization calls for mathematical models of political systems. In this paper, we aim at modeling the time evolution of a…