@article{29543,
  author       = {{Djema, Walid and Giraldi, Laetitia and Maslovskaya, Sofya and Bernard, Olivier}},
  issn         = {{0005-1098}},
  journal      = {{Automatica}},
  keywords     = {{Electrical and Electronic Engineering, Control and Systems Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Turnpike features in optimal selection of species represented by quota models}}},
  doi          = {{10.1016/j.automatica.2021.109804}},
  volume       = {{132}},
  year         = {{2021}},
}

@article{10593,
  abstract     = {{We present a new framework for optimal and feedback control of PDEs using Koopman operator-based reduced order models (K-ROMs). The Koopman operator is a linear but infinite-dimensional operator which describes the dynamics of observables. A numerical approximation of the Koopman operator therefore yields a linear system for the observation of an autonomous dynamical system. In our approach, by introducing a finite number of constant controls, the dynamic control system is transformed into a set of autonomous systems and the corresponding optimal control problem into a switching time optimization problem. This allows us to replace each of these systems by a K-ROM which can be solved orders of magnitude faster. By this approach, a nonlinear infinite-dimensional control problem is transformed into a low-dimensional linear problem. Using a recent convergence result for the numerical approximation via Extended Dynamic Mode Decomposition (EDMD), we show that the value of the K-ROM based objective function converges in measure to the value of the full objective function. To illustrate the results, we consider the 1D Burgers equation and the 2D Navier–Stokes equations. The numerical experiments show remarkable performance concerning both solution times and accuracy.}},
  author       = {{Peitz, Sebastian and Klus, Stefan}},
  issn         = {{0005-1098}},
  journal      = {{Automatica}},
  pages        = {{184--191}},
  title        = {{{Koopman operator-based model reduction for switched-system control of PDEs}}},
  doi          = {{10.1016/j.automatica.2019.05.016}},
  volume       = {{106}},
  year         = {{2019}},
}

@article{15741,
  abstract     = {{
In many cyber–physical systems, we encounter the problem of remote state estimation of geo- graphically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors has to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenario}},
  author       = {{Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}},
  issn         = {{0005-1098}},
  journal      = {{Automatica}},
  title        = {{{Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}}},
  doi          = {{10.1016/j.automatica.2019.108759}},
  year         = {{2019}},
}

@article{35583,
  author       = {{Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}},
  issn         = {{0005-1098}},
  journal      = {{Automatica}},
  keywords     = {{Electrical and Electronic Engineering, Control and Systems Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}}},
  doi          = {{10.1016/j.automatica.2019.108759}},
  volume       = {{113}},
  year         = {{2019}},
}

@article{35584,
  author       = {{Ding, Kemi and Ren, Xiaoqiang and Quevedo, Daniel E. and Dey, Subhrakanti and Shi, Ling}},
  issn         = {{0005-1098}},
  journal      = {{Automatica}},
  keywords     = {{Electrical and Electronic Engineering, Control and Systems Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Defensive deception against reactive jamming attacks in remote state estimation}}},
  doi          = {{10.1016/j.automatica.2019.108680}},
  volume       = {{113}},
  year         = {{2019}},
}