Searched for: subject%3A%22robustness%22
(1 - 9 of 9)
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Nguyen, Viet Anh (author), Shafieezadeh-Abadeh, Soroosh (author), Kuhn, Daniel (author), Mohajerin Esfahani, P. (author)
We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator—that is, a measurable function of the observation—and a fictitious adversary...
journal article 2023
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Sarafraz, Mohammad Saeed (author), Proskurnikov, Anton V. (author), Tavazoei, Mohammad Saleh (author), Mohajerin Esfahani, P. (author)
In this article, we investigate the problem of practical output regulation, i.e., to design a controller that brings the system output in the vicinity of a desired target value while keeping the other variables bounded. We consider uncertain systems that are possibly nonlinear and the uncertainty of their linear parts is modeled element wise...
journal article 2022
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Nguyen, Viet Anh (author), Kuhn, Daniel (author), Mohajerin Esfahani, P. (author)
We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst case (maximum) of Stein’s loss across all normal reference distributions within a...
journal article 2022
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Gravell, Benjamin (author), Mohajerin Esfahani, P. (author), Summers, Tyler H. (author)
The linear quadratic regulator (LQR) problem has reemerged as an important theoretical benchmark for reinforcement learning-based control of complex dynamical systems with continuous state and action spaces. In contrast with nearly all recent work in this area, we consider multiplicative noise models, which are increasingly relevant because...
journal article 2021
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van Parys, Bart P.G. (author), Mohajerin Esfahani, P. (author), Kuhn, Daniel (author)
We study stochastic programs where the decision maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transforms the data to an estimate of the expected cost function under the unknown data...
journal article 2021
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Gravell, Benjamin J. (author), Mohajerin Esfahani, P. (author), Summers, Tyler H. (author)
Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for discrete-time systems and employ them for robust control design. Specifically, we examine a multiplicative noise framework which models the inherent uncertainty and...
journal article 2021
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Pan, K. (author), Palensky, P. (author), Mohajerin Esfahani, P. (author)
Developing advanced diagnosis tools to detect cyber attacks is the key to security of power systems. It has been shown that multivariate data injection attacks can bypass bad data detection schemes typically built on static behavior of the systems, which misleads operators to disruptive decisions. In this article, we depart from the existing...
journal article 2020
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Sharifi K., Arman (author), Bregman, S.C. (author), Mohajerin Esfahani, P. (author), Keviczky, T. (author)
In this paper, we propose an event-based sampling policy to implement a constraint-tightening, robust MPC method. The proposed policy enjoys a computationally tractable design and is applicable to perturbed, linear time-invariant systems with polytopic constraints. In particular, the triggering mechanism is suitable for plants with no...
journal article 2020
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Shaéezadeh-Abadeh, Soroosh (author), Kuhn, Daniel (author), Mohajerin Esfahani, P. (author)
The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, overfitting is typically mitigated by adding regularization terms to the objective that...
journal article 2019
Searched for: subject%3A%22robustness%22
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