Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Kargin, Taylan"'
We study the problem of mixed $\mathit{H}_2/\mathit{H}_\infty$ control in the infinite-horizon setting. We identify the optimal causal controller that minimizes the $\mathit{H}_2$ cost of the closed-loop system subject to an $\mathit{H}_\infty$ const
Externí odkaz:
http://arxiv.org/abs/2409.20020
We explore the infinite-horizon Distributionally Robust (DR) linear-quadratic control. While the probability distribution of disturbances is unknown and potentially correlated over time, it is confined within a Wasserstein-2 ball of a radius $r$ arou
Externí odkaz:
http://arxiv.org/abs/2408.06230
This paper investigates the distributionally robust filtering of signals generated by state-space models driven by exogenous disturbances with noisy observations in finite and infinite horizon scenarios. The exact joint probability distribution of th
Externí odkaz:
http://arxiv.org/abs/2407.18837
We study the infinite-horizon distributionally robust (DR) control of linear systems with quadratic costs, where disturbances have unknown, possibly time-correlated distribution within a Wasserstein-2 ambiguity set. We aim to minimize the worst-case
Externí odkaz:
http://arxiv.org/abs/2406.07248
We consider the rate-distortion function for lossy source compression, as well as the channel capacity for error correction, through the lens of distributional robustness. We assume that the distribution of the source or of the additive channel noise
Externí odkaz:
http://arxiv.org/abs/2405.06528
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 difference in cost obtained by a causal controller and a clairvoyant contr
Externí odkaz:
http://arxiv.org/abs/2312.17376
This paper studies uncertainty set estimation for unknown linear systems. Uncertainty sets are crucial for the quality of robust control since they directly influence the conservativeness of the control design. Departing from the confidence region an
Externí odkaz:
http://arxiv.org/abs/2309.14648
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 regret in LQR cost between a causal and non-causal controller and aim
Externí odkaz:
http://arxiv.org/abs/2307.04966
The stochastic mirror descent (SMD) algorithm is a general class of training algorithms, which includes the celebrated stochastic gradient descent (SGD), as a special case. It utilizes a mirror potential to influence the implicit bias of the training
Externí odkaz:
http://arxiv.org/abs/2210.15323
Thompson Sampling (TS) is an efficient method for decision-making under uncertainty, where an action is sampled from a carefully prescribed distribution which is updated based on the observed data. In this work, we study the problem of adaptive contr
Externí odkaz:
http://arxiv.org/abs/2206.08520