Topics

Event Triggered Control and Estimation

Continuous sensing and communication can be expensive for autonomous systems, especially when many agents must coordinate over shared or unreliable communication channels. Event-triggered and sampled-data strategies reduce this burden by updating only when new information is useful enough to justify communication or actuation.

This page focuses on event-triggered control, event-triggered estimation, and asynchronous sampled-data synchronization. Formation, filtering, and consensus results are included here when the triggering or sampling mechanism is the main contribution.

Event-Triggered Estimation

Distributed estimation problems require agents to fuse intermittent observations and neighbor information while maintaining filter consistency. Event-triggered consensus Kalman filtering studies when communication can be skipped without compromising estimation quality.

Our work develops filter architectures for time-varying graphs, intermittent measurements, and event-based information exchange.

Event-triggered consensus Kalman filtering architecture
Event-triggered information exchange in distributed consensus Kalman filtering.

Representative Publications:

  1. A. Priel and D. Zelazo, “Distributed Consensus Kalman Filtering Over Time-Varying Graphs,” in IFAC World Congress, Yokohama, Japan, Jul. 2023.
    Priel2023a_C.pdf DOI: 10.1016/j.ifacol.2023.10.903 Priel2023a_C.poster Priel2023a_C.bibtex
  2. A. Priel and D. Zelazo, “Event-triggered consensus Kalman filtering for time-varying networks and intermittent observations,” International Journal of Robust and Nonlinear Control, 33(13):7430–7451, 2023.
    Priel2023_J.pdf DOI: https://doi.org/10.1002/rnc.6762 Priel2023_J.bibtex
  3. A. Priel, “Consensus Kalman Filtering: Filter Design and Event-Triggering,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2022.
    Priel2022.pdf Priel2022.bibtex
  4. A. Priel and D. Zelazo, “An Improved Distributed Consensus Kalman Filter Design Approach,” in IEEE Conference on Decision and Control, Austin, Texas, Dec. 2021.
    Priel2021a.pdf Priel2021a.slides DOI: 10.1109/cdc45484.2021.9683438 Priel2021a.bibtex

Event-Based Formation Control

In formation control, event-triggered mechanisms reduce communication while preserving the geometric constraints needed for convergence. The trigger must account for both the agent dynamics and the formation objective, since stale measurements can degrade stability or distort the desired formation.

This work connects event-triggered control with bearing-based formation stabilization and second-order multi-agent dynamics.

Event-triggered bearing formation control response
Event-triggered updates for bearing-based formation stabilization.

Representative Publications:

  1. M. Sewlia and D. Zelazo, “Bearing-Based Formation Stabilization Using Event-Triggered Control,” International Journal on Robust and Nonlinear Control, 34(6):4375–4387, 2024.
    Sewlia2023a_J.pdf DOI: 10.1002/rnc.7185 Sewlia2023a_J.bibtex
  2. M. Sewlia, “Distributed Event-Triggered Control for Multi-Agent Systems with Second-Order Dynamics,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2020.
    Sewlia2020.pdf Sewlia2020.bibtex
  3. M. Sewlia and D. Zelazo, “Distributed Event-Based Control for Second-Order Multi-Agent Systems,” in 27th Mediterranean Conference on Control and Automation, Akko, Israel, Jul. 2019.
    Sewlia2019a.pdf DOI: 10.1109/med.2019.8798577 Sewlia2019a.bibtex

Sampled-Data and Asynchronous Synchronization

Sampled-data synchronization studies how networked systems behave when agents communicate and update at discrete or asynchronous times. Rather than assuming continuous access to neighbor signals, the controller must tolerate sampling, delays, and irregular updates.

Our recent work develops emulation and asynchronous sampled-data approaches for output-feedback synchronization with small communication delays.

Asynchronous sampled-data synchronization response
Sampled-data synchronization under asynchronous communication.

Representative Publications:

  1. G. Barkai, L. Mirkin, and D. Zelazo, “Asynchronous Sampled-Data Synchronization with Small Communications Delays,” in IEEE Conference on Decision and Control, Milan, Italy, Dec. 2024.
    Barkai2024_CDC.pdf Barkai2024_CDC.slides DOI: 10.1109/CDC56724.2024.10886376 Barkai2024_CDC.bibtex
  2. G. Barkai, L. Mirkin, and D. Zelazo, “An Emulation Approach to Output-Feedback Sampled-Data Synchronization,” in European Control Conference, Stockholm, Sweden, Jun. 2024.
    Barkai2024_ECC.pdf Barkai2024_ECC.slides Barkai2024_ECC.bibtex
  3. G. Barkai, L. Mirkin, and D. Zelazo, “Asynchronous Sampled-Data Synchronization with Small communication Delays,” in 63rd Israel Annual Conference on Aerospace Sciences, Haifa, Israel, May 2024.
    Barkai_IACAS2024.pdf Barkai_IACAS2024.bibtex
  4. G. Barkai, L. Mirkin, and D. Zelazo, “An emulation approach to sampled-data synchronization,” in IEEE Conference on Decision and Control, Singapore, Dec. 2023.
    Barkai2023a.pdf DOI: 10.1109/cdc49753.2023.10384079 Barkai2023a.bibtex

Related Publications:

  1. G. Barkai, L. Mirkin, and D. Zelazo, “Asynchronous Sampled-Data Synchronization with Small Communications Delays,” in IEEE Conference on Decision and Control, Milan, Italy, Dec. 2024.
    Barkai2024_CDC.pdf Barkai2024_CDC.slides DOI: 10.1109/CDC56724.2024.10886376 Barkai2024_CDC.bibtex
  2. G. Barkai, L. Mirkin, and D. Zelazo, “An Emulation Approach to Output-Feedback Sampled-Data Synchronization,” in European Control Conference, Stockholm, Sweden, Jun. 2024.
    Barkai2024_ECC.pdf Barkai2024_ECC.slides Barkai2024_ECC.bibtex
  3. G. Barkai, L. Mirkin, and D. Zelazo, “Asynchronous Sampled-Data Synchronization with Small communication Delays,” in 63rd Israel Annual Conference on Aerospace Sciences, Haifa, Israel, May 2024.
    Barkai_IACAS2024.pdf Barkai_IACAS2024.bibtex
  4. M. Sewlia and D. Zelazo, “Bearing-Based Formation Stabilization Using Event-Triggered Control,” International Journal on Robust and Nonlinear Control, 34(6):4375–4387, 2024.
    Sewlia2023a_J.pdf DOI: 10.1002/rnc.7185 Sewlia2023a_J.bibtex
  5. G. Barkai, L. Mirkin, and D. Zelazo, “An emulation approach to sampled-data synchronization,” in IEEE Conference on Decision and Control, Singapore, Dec. 2023.
    Barkai2023a.pdf DOI: 10.1109/cdc49753.2023.10384079 Barkai2023a.bibtex
  6. A. Priel and D. Zelazo, “Distributed Consensus Kalman Filtering Over Time-Varying Graphs,” in IFAC World Congress, Yokohama, Japan, Jul. 2023.
    Priel2023a_C.pdf DOI: 10.1016/j.ifacol.2023.10.903 Priel2023a_C.poster Priel2023a_C.bibtex
  7. A. Priel and D. Zelazo, “Event-triggered consensus Kalman filtering for time-varying networks and intermittent observations,” International Journal of Robust and Nonlinear Control, 33(13):7430–7451, 2023.
    Priel2023_J.pdf DOI: https://doi.org/10.1002/rnc.6762 Priel2023_J.bibtex
  8. A. Priel, “Consensus Kalman Filtering: Filter Design and Event-Triggering,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2022.
    Priel2022.pdf Priel2022.bibtex
  9. A. Priel and D. Zelazo, “An Improved Distributed Consensus Kalman Filter Design Approach,” in IEEE Conference on Decision and Control, Austin, Texas, Dec. 2021.
    Priel2021a.pdf Priel2021a.slides DOI: 10.1109/cdc45484.2021.9683438 Priel2021a.bibtex
  10. M. Sewlia, “Distributed Event-Triggered Control for Multi-Agent Systems with Second-Order Dynamics,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2020.
    Sewlia2020.pdf Sewlia2020.bibtex
  11. M. Sewlia and D. Zelazo, “Distributed Event-Based Control for Second-Order Multi-Agent Systems,” in 27th Mediterranean Conference on Control and Automation, Akko, Israel, Jul. 2019.
    Sewlia2019a.pdf DOI: 10.1109/med.2019.8798577 Sewlia2019a.bibtex