Dr. Maestre will give a talk on Data Based Distributed Model Predictive Control at the IFAC Workshop on Game Theory and Learning for Cyber-Physical Systems, organized by Tansu Alpcan, Tamer Basar, Quanyan Zhu, and himself.
Also, he is one of the organizers of the Open Invited Track on Predictive Control of Large-Scale Water Systems, which was proposed in cooperation with Eric Duviella, Laurent Lefevre, and Carlos Ocampo-Martinez.
Finally, the following papers were accepted at the conference and will be presented by members of our team:
Eva Masero Rubio, J. M. Maestre, Mario Francisco, Eduardo F. Camacho Coalitional MPC with predicted topology transitions
Francisco López Rodríguez, J. M. Maestre, Francisco Javier Muros, Eduardo F. Camacho A Modular Feedback Approach for Distributed Control
Francisco Javier Muros, J. M. Maestre An LMI-Based Approach for Semivalues Constraints in Coalitional Feedback Control
Paula Chanfreut, J. M. Maestre, Quanyan Zhu, Eduardo F. Camacho No-Regret Learning for Coalitional Model Predictive Control
Daniel Saracho, Francisco Javier Muros, J. M. Maestre Efficient Design of Fault Detection Architectures for Power Networks by Using Game Theory
Paul Trodden, J. M. Maestre, Hideaki Ishii Actuation attacks on constrained linear systems: a set-theoretic analysis
Hadger Gedachi, Paula Chanfreut, J. M. Maestre A nonlinear distributed model predictive scheme for systems based on Hammerstein model
Paula Chanfreut, Twan Keijzer, Riccardo M.G. Ferrari, J. M. Maestre A Topology-Switching Coalitional Control and Observation Scheme with Stability Guarantees
Carmen Amo Alonso, Dimitar Ho, J. M. Maestre Distributed Linear Quadratic Regulator Robust toCommunication Dropouts
