Cooperative Consensus-based Control of Multi-agent Systems over Wireless Channels

PIs: Jörg Raisch (TU Berlin), Slawomir Stanczak (TU Berlin)

The central objective of this proposal is to investigate and exploit trade-offs and synergies involving control and communication in the context of cooperative control of multiagent systems over wireless channels. In multiagent systems, a potentially large number of entities (“agents”) operate in a shared environment and need to jointly solve tasks while observing restrictions regarding communication (and other) resources. There are numerous application scenarios, ranging from groups of terrestrial vehicles to swarms of unmanned aerial vehicles employed for, e.g., monitoring air or water quality, detecting forest fires, etc. Distributed control of such systems has been recognized as an important topic, asmonolithic control approaches exhibit a number of practically important disadvantages. Such distributed control schemes typically feature both local control and a cooperative control level, often based on consensus mechanisms. Whereas in the former, local measurement information is fed back to local actuators, the latter involves information exchange between agents, typically over wireless networks. In this scenario, there are a number of mutually related trade-offs: (i) control versus communication on the cooperative level, (ii) local versus cooperative control, and (iii) trade-off involving communication aspects, as, e.g., accuracy and delay. These trade-offs clearly impact on the communication demand imposed by control. Strong emphasis will be on matching the communication supply to this demand. E.g., as agents typically do not need to know individual specific information from other agents but rather to evaluate a function of this information, superposition in transmission channels may in fact become a property that allows drastic savings in communication.

Involved PhD candidates

Publications

  1. Sebastian Gallenmüller et al: Benchmarking networked control systems, 2018 IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems (CPSBench). IEEE. 2018, pp. 7–12.
  2. Fabio Molinari and Jörg Raisch: Automation of road intersections using consensus-based auction algorithms, 2018 Annual American Control Conference (ACC). IEEE. 2018, pp. 5994–6001.
  3. Fabio Molinari, Slawomir Stanczak, and Jorg Raisch: Exploiting the superposition property of wireless communication for average consensus problems in multi-agent systems, 2018 European Control Conference (ECC). IEEE. 2018, pp. 1766–1772.
  4. Fabio Molinari, S lawomir Sta´nczak, and Jörg Raisch: Exploiting the superposition property of wireless communication for max-consensus problems in multi-agent systems, IFAC-PapersOnLine 51.23 (2018), pp. 176–181.
  5. Navneet Agrawal, Matthias Frey, and S lawomir Sta´nczak: A scalable max-consensus protocol for noisy ultra-dense networks, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE. 2019, pp. 1–5.
  6. Vittorio Lippi, Fabio Molinari, and Thomas Seel: Distributed bio-inspired humanoid posture control, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. 2019, pp. 5360–5365.
  7. Fabio Molinari, Alexander Martin Dethof, and Jörg Raisch: Traffic automation in urban road networks using consensus-based auction algorithms for road intersections, 2019 18th European Control Conference (ECC). IEEE. 2019, pp. 3008–3015.
  8. Fabio Molinari and Jörg Raisch: Efficient consensus-based formation control with discrete-time broadcast updates, 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE. 2019, pp. 4172– 4177.
  9. Fabio Molinari et al: Automating lane changes and collision avoidance on highways via distributed agreement, at-Automatisierungstechnik 67.12 (2019), pp. 1047–1057
  10. Zenit Music et al: Design of a networked controller for a two-wheeled inverted pendulum robot, IFAC-PapersOnLine 52.20 (2019), pp. 169–174.
  11. Michael Meindl et al: Overcoming output constraints in iterative learning control systems by reference adaptation, IFAC-PapersOnLine 53.2 (2020), pp. 1480–1486.
  12. Fabio Molinari, Alexander Katriniok, and Jörg Raisch: Real-time distributed automation of road intersections, IFAC-PapersOnLine 53.2 (2020), pp. 2606–2613.
  13. Fabio Molinari and Jörg Raisch: Exploiting wireless interference for distributively solving linear equations, IFAC-PapersOnLine 53.2 (2020), pp. 2999–3006.
  14. Samuele Zoppi et al: NCSbench: Reproducible Benchmarking Platform for Networked Control Systems, 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC). IEEE. 2020, pp. 1–9.
  15. Navneet Agrawal, Renato LG Cavalcante, and S lawomir Sta´nczak: Adaptive Estimation of Angular Power Spectra for Time-Varying MIMO Channels, 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE. 2021, pp. 96–100.
  16. Michael Meindl et al: Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks, IEEE Transactions on Control Systems Technology (2021).
  17. Fabio Molinari et al: Max-consensus over fading wireless channels, IEEE Transactions on Control of Network Systems 8.2 (2021), pp. 791–802.
  18. Davide Zorzenon, Fabio Molinari, and Jörg Raisch: Low Complexity Method for Simulation of Epidemics Based on Dijkstra’s Algorithm, 2021 American Control Conference (ACC). IEEE. 2021, pp. 3018–3025.
  19. Navneet Agrawal et al: A Learning-Based Approach to Approximate Coded Computation, 2022 IEEE Information Theory Workshop (ITW). IEEE. 2022, pp. 600–605.
  20. Angelos Koumpis, Davide Zorzenon, and Fabio Molinari: Automation Of Roundabouts Via Consensus-based Distributed Auctions and Stochastic Model Predictive Control, 2022 European Control Conference (ECC). IEEE. 2022, pp. 14–20.
  21. Fabio Molinari et al: Over-The-Air Max-Consensus in Clustered Networks Adopting Half-Duplex Communication Technology, IEEE Transactions on Control of Network Systems (2022).
  22. Navneet Agrawal, Renato L. G. Cavalcante, and S lawomir Sta´nczak: Dynamic Distributed Convex Optimization ”Over-The-Air” In Decentralized Wireless Networks, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2023, pp. 1–5. doi: 10.1109/ICASSP49357.2023.10096916.
  23. Navneet Agrawal et al: Distributed Convex Optimization “Over-the-Air” in Time-Varying Environments, (submitted) IEEE Transactions on Signal Processing Over Networks (2023).
  24. Jiepeng Tang et al: Coded Distributed Image Classification, (accepted) MLSP 2023 - IEEE International Workshop on Machine Learning for Signal Processing (MLSP) (2023).