Model-Predictive Cyber-Physical Networking

PIs: Olaf Stursberg (Universität Kassel), Gerhard Wunder (FU Berlin)

Cyber-Physical Systems (CPS) integrate computation, networking, and physical processes: embedded computers and networks monitor and control the physical processes, and the physical processes vice versa affect the computations. Yet, while computation and physical processes are nowadays often melted into a virtual cyber-physical reality, development of controllers and communication networks is typically strictly separated. This project aims at creating a framework of model predictive cyber-physical networking, which extends principles and properties known in the control community under the term model predictive control (MPC) to CPS with explicit representation of the communication network. The ability of MPC to predict and incorporate planned future behavior (and corresponding deviations thereof) into the design of feedback controllers makes it a natural candidate for a unified framework, which includes the prediction of network effects and controls, communication, and the physical part of the CPS with similar means.

Involved PhD candidates

  • Richard Schöffauer: Researcher and full time PhD student at FU Berlin since April 2017, diplomas in mechanical and electrical engineering from TU Dresden with focus on applied mechanics and control theory
  • Jannik Hahn: Researcher and full time PhD student at University of Kassel since January 2018, B. Sc and M. Sc in electrical engeneering with focus on control and system theory from University of Kassel

Publications

  1. R. Schoeffauer, G. Wunder: Predictive Network Control and Throughput Sub-Optimality of MaxWeight, European Conference on Networks and Communications 2018 (EuCNC 2018)
  2. J. Hahn, R. Schoeffauer, G. Wunder, O. Stursberg: Distributed MPC with Prediction of Time-Varying Communication Delay, 7th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys 2018)
  3. R. Schoeffauer, G. Wunder: A linear algorithm for reliable predictive network control, 37th IEEE Global Communications Conference (GLOBECOM 2018)