Network-Informed Control - Control-Informed Network (NICCI)

PIs: Rolf Findeisen (Otto von Guericke U Magdeburg), Holger Karl (U Paderborn), Daniel Quevedo (U Paderborn)

In this project, we strive to design a controller and network resource management unit separately, but still they exchange requirements capabilities and configurations messages. One of the main challenges would be, given the limited data rate, delays and erroneous transmission, how can one guarantee safe and reliable operation of the system controlled visa wireless network? (Comprehensive project description can be found here.)

Planned Contributions

  • Determine the feasibility and desirability of a close, information-exchange-driven cooperation between controllers and network resource management.
  • Develop architectural models (including interfaces, data formats, and protocol definitions) for this integration, ensuring modularity and generality rather than problem-specific design.
  • Develop core algorithms for this architecture, e.g., compression of diversity-based network capabilities meaningful for control, controllers which make best use of and adapt to changing network capabilities.
  • Integrate prediction techniques, for both control demand and channel state, in the overall system design.
  • Evaluate the resulting architecture and algorithms by analysis and simulation

Involved PhD candidates and PostDocs

  • Binyam Shiferaw Heyi: Education: (2008): BSc in Electrical and Computer Engineering, Addis Ababa University, Addis Ababa, Ethiopia. (2013): MSc in Network Services and Systems, KTH-The Royal Institute of Technology, Stockholm, Sweden. Work Experience: (Jan 2017-present) Research Assistant, Computer Network Group, Paderborn University, Paderborn, Germany. (Sept 2013-Dec 2016): Lecturer, School Electrical Engineering and Computing, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia, (Feb 2012-Jan 2013): Network Engineer, MarnaTech AB (Now FlowScape AB), Stockholm, Sweden.

Publications

  1. Shiferaw Heyi B, Karl H.: Modelling Time-Limited Capacity of a Wireless Channel as a Markov Reward Process, Proc. of IEEE Wireless Communications and Networking Conference (WCNC), 2018.
  2. S. Blasi, M. Kögel, and R. Findeisen. : Distributed Model Predictive Control Using Cooperative Contract Options. 6th IFAC Conference on Nonlinear Model Predictive Control (NMPC), 2018.
  3. T. Bäthge, M. Kögel, S. Di Cairano, and R. Findeisen. Contract-Based Predictive Control for Modularity in Hierarchical Systems. 6th IFAC Conference on Nonlinear Model Predictive Control (NMPC), 2018.
  4. M. Kögel and R. Findeisen. Towards Optimal Tuning of Robust Output Feedback MPC. 6th IFAC Conference on Nonlinear Model Predictive Control (NMPC), 2018.
  5. B. Demirel, A. Ramaswamy, D. E. Quevedo and H. Karl, ”DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling,” Proceedings of the 57th IEEE Conference on Decision and Control, Miami Beach, FL, USA, 2018.
  6. S. H. Mousavi, N. Noroozi, R. Geiselhart M. Kögel, and R. Findeisen. On Integral Input-To-State Stability of Event-Triggered Control Systems. IEEE Conference on Decision and Control (CDC), 2019.
  7. M. Kögel, D. E. Quevedo, and R. Findeisen. Combined control and communication scheduling for constrained systems using robust output feedback MPC. European Control Conference (ECC), 2019.
  8. M. Kögel and R. Findeisen. Combined online communication scheduling and output feedback MPC of cyber-physical systems. 1st IEEE Workshop on Cyber-Physical Networking (CPN) at the IEEE Consumer Communications & Networking Conference (CCNC), 2019.
  9. A. Redder, A. Ramaswamy and D. E. Quevedo,Deep reinforcement learning for scheduling in large-scale networked control systems, Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys 2019), Chicago, IL, USA, 2019.
  10. M. Koegel, D. E. Quevedo and R. Findeisen, Combined control and communication scheduling for constrained system using robust output feedback MPC, Proceedings of the European Control Conference, Naples, Italy, 2019.
  11. M. Kögel and R. Findeisen. Fusing multiple time varying tubes for robust MPC. IFAC World Congress (IFAC WC), 2020.
  12. B. Morabito, M. Kögel, S. Blasi, V. Klemme, C. Hansen, O. Höhn, and R. Findeisen. Multi-stage Event-triggered Model Predictive Control for Automated Trajectory Drilling. IFAC World Congress (IFAC WC), 2020.
  13. M. Ibrahim, M. Kögel, C. Kallies, and R. Findeisen. Contract-based Hierarchical Model Predictive Control and Planning for Autonomous Vehicle. IFAC World Congress (IFAC WC), 2020.
  14. M. Kögel and R. Findeisen. Robust MPC with reduced conservatism by blending multiple tubes. American Control Conference (ACC), 2020.
    1. A. Leong, A. Ramaswamy, D. E. Quevedo, H. Karl and L. Shi, Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems, Automatica, vol. 113, Mar. 2020.
  15. Muhammad Sohaib Amjad, Max Schettler, Sigrid Dimce and Falko Dressler, Inband Full-Duplex Relaying for RADCOM-based Cooperative Driving, Proceedings of 12th IEEE Vehicular Networking Conference (VNC 2020), Virtual Conference, December 2020.
  16. M. Kögel, D. E. Quevedo, and R. Findeisen. Robust MPC for networks with varying communication capabilities. IFAC Conference on Nonlinear Model Predictive Control (NMPC), 2021.
  17. M. Kögel and R. Findeisen, Robust output feedback MPC with reduced conservatism for linear uncertain systems using time varying tubes, Proceedings of the IEEE Conference on Decision and Control (CDC), 2021
  18. M. Kögel and R. Findeisen, Reducing conservatism in stochastic model predictive blending multiple control gains, Proceedings of the American Control Conference (ACC), 2021
  19. A. Ramaswamy, S. Bhatnagar and D. E. Quevedo, Asynchronous Stochastic Approximations with Asymptotically Biased Errors and Deep Multi-agent Learning, IEEE Transactions on Automatic Control, vol. 66, no. 9, pp. 3969–3983, Sep. 2021
  20. Florian Klingler, Max Schettler, Sigrid Dimce, Muhammad Sohaib Amjadand Falko Dressler, mmWave on the Road – Field Testing IEEE 802.11ad WLAN at 60 GHz, Proceedings of 40th IEEE International Conference on Computer Communications (INFOCOM 2021), Poster Session, Virtual Conference, May 2021.
  21. Sigrid Dimce, Muhammad Sohaib Amjad and Falko Dressler, mmWave on the Road: Investigating the Weather Impact on 60 GHz V2X Communication Channels, Proceedings of 16th IEEE/IFIP Conference on Wireless On demand Network Systems and Services (WONS 2021), Virtual Conference, March 2021.
  22. A. Ramaswamy, A. Redder and D. E. Quevedo, Optimization Over Time-Varying Networks and Unbounded Information Delays, IEEE Transactions on Automatic Control, vol. 67, vol. 8, pp. 4131–4137, August 2022.
  23. Justin M. Kennedy, Julian Heinovski, Daniel E. Quevedo and Falko Dressler, Centralized Model-Predictive Control with Human-Driver Interaction for Platooning, arXiv, cs.MA, 2205.09259, May 2022.
  24. Michele Segata, Renato Lo Cigno, Tobias Hardes, Julian Heinovski, Max Schettler, Bastian Bloessl, Christoph Sommer and Falko Dressler, Multi-Technology Cooperative Driving: An Analysis Based on PLEXE, IEEE Transactions on Mobile Computing (TMC), February 2022.
  25. Justin M. Kennedy, Julian Heinovski, Daniel E. Quevedo and Falko Dressler, Centralized Model-Predictive Control with Human-Driver Interaction for Platooning, arXiv, cs.MA, 2205.09259, May 2022.

Special Publications

  • D. Quevedo: Privacy and Security of Cyber-physical Systems, International Journal of Robust and Nonlinear Control (Guest Editor)
  • D. Quevedo: Feedback Control for the Internet of Things, IEEE Internet of Things Journal (Guest Editor)