Control of Multi-Agent Systems

Multi-agent systems are large-scale systems comprised of a group of coupled dynamic units, such as power generation sources in a power distribution network or a team of autonomous and unmanned vehicles. These systems interact via an exchange of information over a communication and sensing network. The complexity of this general class of problems arises from the heterogeneous dynamics of the systems comprising it, the diversity of interaction and communication mediums, and their scale in terms of the number of interacting systems and system interconnections. While research in this area is very active within the controls community, there remain many challenging and open problems that must be addressed before considering this a complete theory. The fundamental research questions we are looking at are:

  1. How does the underlying connection topology of networked dynamic systems affect its systems-theoretic properties?
  2. Can the connection topology be designed in conjunction with other synthesis techniques and tools used for dynamic systems?

We explore many different research questions in this area. Below is a sample of some of our contributions.

Consensus Algorithms

A fundamental task in many multi-agent coordination problems is the ability of the agents to distributedly agree on some quantity of interest. This may include agreeing on a common heading and speed for autonomous vehicles, opinions in social networks, or estimates of measured quantities. Our works have explored how the information exchange structure between agents influences the performance of these consensus algorithms.

Consensus trajectories
Trajectories of a consensus protocol.

Selected Publications:

  1. G. Barkai, L. Mirkin, and D. Zelazo, “On two-degrees-of-freedom agreement protocols,” Nov. 2025.
    Barkai2026_ECC.pdf arXiv: https://arxiv.org/abs/2511.12632 Barkai2026_ECC.bibtex
  2. F. Yue and D. Zelazo, “A Passivity Analysis for Nonlinear Consensus on Digraphs,” 2025.
    Yue2025_CDC.pdf Yue2025_CDC.slides arXiv: https://arxiv.org/abs/2508.21428 Yue2025_CDC.bibtex
  3. G. Barkai, L. Mirkin, and D. Zelazo, “On Sampled-Data Consensus: Divide and Concur,” IEEE Control Systems Letters, 6:343–348, 2022.
    Barkai2022a_J.pdf DOI: 10.1109/lcsys.2021.3074589 Barkai2022a_J.bibtex
  4. D. Zelazo, M. Mesbahi, and M.-A. Belabbas, “Graph Theory in Systems and Controls,” in IEEE Conference on Decision and Control, Miami, Florida, Dec. 2018.
    Zelazo2018a.pdf Zelazo2018a.slides DOI: 10.1109/cdc.2018.8619841 Zelazo2018a.bibtex
  5. M. H. Trinh, D. Zelazo, Q. V. Tran, and H.-S. Ahn, “Pointing Consensus for Rooted Out-Branching Graphs,” in American Control Conference, Milwaukee, WI, Jun. 2018.
    Trinh2018a.pdf DOI: 10.23919/acc.2018.8430992 Trinh2018a.bibtex
  6. N. Leiter and D. Zelazo, “Graph-Based Model Reduction of the Controlled Consensus Protocol,” in IFAC World Congress, Toulouse, France, Jul. 2017.
    Leiter2017a.pdf DOI: 10.1016/j.ifacol.2017.08.1467 Leiter2017a.bibtex
  7. D. Zelazo, S. Schuler, and F. Allgöwer, “Performance and Design of Cycles in Consensus Networks,” Systems & Control Letters, 62(1):85–96, 2013.
    Zelazo2011_J.pdf DOI: 10.1016/j.sysconle.2012.10.014 Zelazo2011_J.bibtex
  8. D. Zelazo and F. Allgöwer, “Eulerian Consensus Networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, Dec. 2012.
    Zelazo2012a.pdf Zelazo2012a.slides DOI: 10.1109/CDC.2012.6425921 Zelazo2012a.bibtex
  9. D. Zelazo and M. Mesbahi, “Edge Agreement: Graph-Theoretic Performance Bounds and Passivity Analysis,” IEEE Transactions on Automatic Control, 56(3):544–555, 2011.
    Zelazo2009b_J.pdf DOI: 10.1109/TAC.2010.2056730 Zelazo2009b_J.bibtex
  10. D. Zelazo, “Graph-theoretic Methods for the Analysis and Synthesis of Networked Dynamic Systems,” phdthesis, University of Washington, Department of Aeronautics & Astronautics, 2009.
    Zelazo2010.pdf Zelazo2010.bibtex

Network Identification

Many large scale networks are often designed with hopes of plug-and-play behavior. In other applications, agents in a network may be vulnerable to attack or failure resulting in changes to the network structure and behavior. As a result, the network structure may not be known. It is of interest, therefore, to try to estimate or recover the network structure using only limited measurements from the network itself. This is known as the network identification problem.

Network Identification
Fault identification in networks.

Selected Publications:

  1. M. Sharf and D. Zelazo, “Network Identification for Diffusively-Coupled Networks with Minimal Time Complexity,” IEEE Transactions on Control of Network Systems, 10(3):1616–1628, 2023.
    Sharf2023a_J.pdf DOI: https://doi.org/10.1109/TCNS.2023.3237368 Sharf2023a_J.bibtex
  2. D. Zelazo, M. Fabris, and L. Peled-Eitan, “Distributed Identification of Leader Agents in Semi-Autonomous Networks,” in 62nd Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Mar. 2023.
    Zelazo2023b_C.pdf Zelazo2023b_C.slides Zelazo2023b_C.bibtex
  3. M. Sharf and D. Zelazo, “Monitoring Link Faults in Nonlinear Diffusively-coupled Networks,” IEEE Transactions on Automatic Control, 67(6):2857–2872, 2022.
    Sharf2019d_J.pdf DOI: 10.1109/tac.2021.3095258 Sharf2019d_J.bibtex
  4. M. Sharf and D. Zelazo, “Network Identification: A Passivity and Network Optimization Approach,” in IEEE Conference on Decision and Control, Miami, Florida, Dec. 2018.
    Sharf2018a.pdf DOI: 10.1109/cdc.2018.8619059 Sharf2018a.bibtex

All our publications in this area can be found below:

Related Publications:

  1. G. Barkai, L. Mirkin, and D. Zelazo, “On two-degrees-of-freedom agreement protocols,” Nov. 2025.
    Barkai2026_ECC.pdf arXiv: https://arxiv.org/abs/2511.12632 Barkai2026_ECC.bibtex
  2. E. Matmon and D. Zelazo, “Fiedler-Based Characterization and Identification of Leaders in Semi-Autonomous Networks,” Nov. 2025.
    Matmon2026_ECC.pdf arXiv: https://arxiv.org/abs/2511.02317 Matmon2026_ECC.bibtex
  3. F. Yue and D. Zelazo, “A Passivity Analysis for Nonlinear Consensus on Balanced Digraphs,” European Journal of Control, 86(Part A):101368, 2025.
    Yue2025_EJC.pdf Yue2025_EJC.slides DOI: 10.1016/j.ejcon.2025.101368 Yue2025_EJC.bibtex
  4. F. Yue and D. Zelazo, “A Passivity Analysis for Nonlinear Consensus on Digraphs,” in IAAC3 Control Conference, Herzliya, Israel, Apr. 2025.
    Yue_IAAC2025.slides Yue_IAAC2025.bibtex
  5. E. Matmon and D. Zelazo, “Leader Identification in Semi-Autonomous Consensus Protocols,” in IAAC3 Control Conference, Herzliya, Israel, Apr. 2025.
    Matmon_IAAC2025.slides Matmon_IAAC2025.bibtex
  6. G. Barkai and D. Zelazo, “On Filtered Consensus Protocols,” in IAAC3 Control Conference, Herzliya, Israel, Apr. 2025.
    Barkai_IAAC2025.bibtex
  7. F. Yue and D. Zelazo, “A Passivity Analysis for Nonlinear Consensus on Digraphs,” 2025.
    Yue2025_CDC.pdf Yue2025_CDC.slides arXiv: https://arxiv.org/abs/2508.21428 Yue2025_CDC.bibtex
  8. M. Sharf and D. Zelazo, “Cluster assignment in multi-agent systems: Sparsity bounds and fault tolerance,” Asian Journal of Control, 27(1):63–75, 2025.
    Sharf2025a_J.pdf DOI: 10.1002/asjc.3149 Sharf2025a_J.bibtex
  9. 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
  10. 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
  11. J. Shi and D. Zelazo, “Bearing-only Formation Control with Directed Sensing,” in 63rd Israel Annual Conference on Aerospace Sciences, Haifa, Israel, May 2024.
    Shi_IACAS2024.pdf Shi_IACAS2024.slides Shi_IACAS2024.bibtex
  12. J. Attias, Y. Marciano, R. Arhipov, and D. Zelazo, “An Open Source Quadcopter Platform for Simulink,” in 63rd Israel Annual Conference on Aerospace Sciences, Haifa, Israel, May 2024.
    Attias_IACAS2024.pdf Attias_IACAS2024.slides Attias_IACAS2024.bibtex
  13. F. Yue and D. Zelazo, “Diodes and the Importance of Network Orientations in Diffusively-Coupled Networks,” in 63rd Israel Annual Conference on Aerospace Sciences, Haifa, Israel, May 2024.
    Yue_IACAS2024.pdf Yue_IACAS2024.slides Yue_IACAS2024.bibtex
  14. 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
  15. 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
  16. G. Barkai, L. Mirkin, and D. Zelazo, “On the internal stability of diffusively coupled multi-agent systems and the dangers of cancel culture,” Automatica, 155:111158, 2023.
    Barkai2023a_J.pdf DOI: https://doi.org/10.1016/j.automatica.2023.111158 Barkai2023a_J.bibtex
  17. M. Sharf and D. Zelazo, “Network Identification for Diffusively-Coupled Networks with Minimal Time Complexity,” IEEE Transactions on Control of Network Systems, 10(3):1616–1628, 2023.
    Sharf2023a_J.pdf DOI: https://doi.org/10.1109/TCNS.2023.3237368 Sharf2023a_J.bibtex
  18. M.-A. Belabbas, X. Chen, and D. Zelazo, “On Structural Rank and Resilience of Sparsity Patterns,” IEEE Transactions on Automatic Control, 68(8):4783–4795, 2023.
    Belabbas2021a_J.pdf DOI: 10.1109/tac.2022.3212013 Belabbas2021a_J.bibtex
  19. 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
  20. D. Zelazo, M. Fabris, and L. Peled-Eitan, “Distributed Identification of Leader Agents in Semi-Autonomous Networks,” in 62nd Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Mar. 2023.
    Zelazo2023b_C.pdf Zelazo2023b_C.slides Zelazo2023b_C.bibtex
  21. M. Fabris and D. Zelazo, “A Robustness Analysis to Structured Channel Tampering Over Secure-by-Design Consensus Networks,” IEEE Control Systems Letters, 7:2011–2016, 2023.
    Fabris2023_J.pdf Fabris2023_J.slides DOI: 10.1109/LCSYS.2023.3284482 Fabris2023_J.bibtex
  22. G. Barkai, L. Mirkin, and D. Zelazo, “On Internal Stability of Diffusive-Coupling and the Dangers of Cancel Culture,” in 25th International Symposium on Mathematical Theory of Networks and Systems, Germany, Sep. 2022.
    Barkai2022a.pdf Barkai2022a.slides Barkai2022a.bibtex
  23. M. Fabris and D. Zelazo, “Secure Consensus via Objective Coding: Robustness Analysis to Channel Tampering,” IEEE Transactions on Systems, Man and Cybernetics: Systems, 52(12):7885–7897, 2022.
    Fabris2022a_J.pdf DOI: 10.1109/tsmc.2022.3177756 Fabris2022a_J.bibtex
  24. M. Sharf and D. Zelazo, “Monitoring Link Faults in Nonlinear Diffusively-coupled Networks,” IEEE Transactions on Automatic Control, 67(6):2857–2872, 2022.
    Sharf2019d_J.pdf DOI: 10.1109/tac.2021.3095258 Sharf2019d_J.bibtex
  25. M. Sharf and D. Zelazo, “Cluster Assignment in Multi-Agent Systems,” in The 13th Asian Control Conference, Jeju Island, South Korea, May 2022.
    Sharf2022a.pdf Sharf2022a.slides DOI: 10.23919/ascc56756.2022.9828091 Sharf2022a.bibtex
  26. M. Sharf, A. Romer, D. Zelazo, and F. Allgower, “Model-Free Practical Cooperative Control for Diffusively Coupled Systems,” IEEE Transactions on Automatic Control, 67(2):754–766, 2022.
    Sharf2019e_J.pdf DOI: 10.1109/tac.2021.3056582 Sharf2019e_J.bibtex
  27. G. Barkai, L. Mirkin, and D. Zelazo, “On Sampled-Data Consensus: Divide and Concur,” IEEE Control Systems Letters, 6:343–348, 2022.
    Barkai2022a_J.pdf DOI: 10.1109/lcsys.2021.3074589 Barkai2022a_J.bibtex
  28. N. Leiter, “Graph-based Model Reduction Methods for Multi-Agent Systems,” phdthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2022.
    Leiter2022.pdf Leiter2022.bibtex
  29. A. Priel, “Consensus Kalman Filtering: Filter Design and Event-Triggering,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2022.
    Priel2022.pdf Priel2022.bibtex
  30. 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
  31. M. Sharf, A. Jain, and D. Zelazo, “Geometric Method for Passivation and Cooperative Control of Equilibrium-Independent Passive-Short Systems,” IEEE Transactions on Automatic Control, 66(12):5877–5892, 2021.
    Sharf2019c_J.pdf DOI: 10.1109/tac.2020.3043390 Sharf2019c_J.bibtex
  32. N. Leiter and D. Zelazo, “Edge-matching graph contractions and their interlacing properties,” Linear Algebra and its Applications, 612:289–317, 2021.
    Leiter2021_J.pdf DOI: https://doi.org/10.1016/j.laa.2020.11.003 Leiter2021_J.bibtex
  33. N. Leiter and D. Zelazo, “Product Form of Projection-Based Model Reduction and its Application to Multi-Agent Systems,” 2021.
    arXiv: https://arxiv.org/abs/2112.15182 leiter2021product.bibtex
  34. H. Chen, D. Zelazo, X. Wang, and L. Shen, “Convergence Analysis of Signed Nonlinear Networks,” IEEE Transactions on Control of Network Systems, 7(1):189–200, 2020.
    Chen2018a_J.pdf DOI: 10.1109/tcns.2019.2913550 Chen2018a_J.bibtex
  35. M. Sharf, “Network Optimization Methods in Passivity-Based Cooperative Control,” phdthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2020.
    Sharf2020.pdf Sharf2020.bibtex
  36. Y. Palti, “Deployment Strategies for Coverage Control Problems,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2020.
    Palti2020.pdf Palti2020.bibtex
  37. D. Mukherjee and D. Zelazo, “Robustness of Consensus over Weighted Digraphs,” IEEE Transactions on Network Sciences and Engineering, 6(4):657–670, 2019.
    Muhkerjee2017a_J.pdf DOI: 10.1109/tnse.2018.2866780 Muhkerjee2017a_J.bibtex
  38. D. Mukherjee and D. Zelazo, “Consensus of Higher Order Agents: Robustness and Heterogeneity,” IEEE Transactions on Control of Network Systems, 6(4):1323–1333, 2019.
    Muhkerjee2017b_J.pdf DOI: 10.1109/tcns.2018.2889003 Muhkerjee2017b_J.bibtex
  39. M. Sharf and D. Zelazo, “Analysis and Synthesis of MIMO Multi-Agent Systems Using Network Optimization,” IEEE Transactions on Automatic Control, 64(11):1558–2523, 2019.
    Sharf2017b_J.pdf DOI: 10.1109/tac.2019.2908258 Sharf2017b_J.bibtex
  40. M. Sharf and D. Zelazo, “Symmetry-Induced Clustering in Multi-Agent Systems using Network Optimization and Passivity,” in 27th Mediterranean Conference on Control and Automation, Akko, Israel, Jul. 2019.
    Sharf2019a.pdf Sharf2019a.slides DOI: 10.1109/med.2019.8798507 Sharf2019a.bibtex
  41. M. Sharf and D. Zelazo, “Network Feedback Passivation of Passivity-Short Multi-Agent Systems,” IEEE Control Systems Letters, 3(3):607–612, 2019.
    Sharf2019a_J.pdf Sharf2019a_J.slides DOI: 10.1109/lcsys.2019.2914128 Sharf2019a_J.bibtex
  42. Y. Palti and D. Zelazo, “A Projected Lloyd’s Algorithm for Coverage Control Problems,” in 59th Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Mar. 2019.
    Palti2019a.pdf Palti2019a.bibtex
  43. D. Zelazo, M. Mesbahi, and M.-A. Belabbas, “Graph Theory in Systems and Controls,” in IEEE Conference on Decision and Control, Miami, Florida, Dec. 2018.
    Zelazo2018a.pdf Zelazo2018a.slides DOI: 10.1109/cdc.2018.8619841 Zelazo2018a.bibtex
  44. A. Jain, M. Sharf, and D. Zelazo, “Regularization and Feedback Passivation in Cooperative Control of Passivity-Short Systems: A Network Optimization Perspective,” IEEE Control Systems Letters, 2(4):731–736, 2018.
    Jain2018a_J.pdf DOI: 10.1109/lcsys.2018.2847738 Jain2018a_J.bibtex
  45. M. H. Trinh, D. Zelazo, Q. V. Tran, and H.-S. Ahn, “Pointing Consensus for Rooted Out-Branching Graphs,” in American Control Conference, Milwaukee, WI, Jun. 2018.
    Trinh2018a.pdf DOI: 10.23919/acc.2018.8430992 Trinh2018a.bibtex
  46. D. Mukherjee and D. Zelazo, “Robust Consensus of Higher Order Agents over Cycle Graphs,” in 58th Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Mar. 2018.
    Mukherjee2016a.pdf Mukherjee2016a.slides Mukherjee2016a.bibtex
  47. N. Leiter and D. Zelazo, “The Aggregating Consensus Protocol: A Case Study of Behavioral Multi-Agent Systems,” in 58th Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Feb. 2018.
    Leiter2018a.pdf Leiter2018a.bibtex
  48. N. Leiter and D. Zelazo, “Graph-Based Model Reduction of the Controlled Consensus Protocol,” in IFAC World Congress, Toulouse, France, Jul. 2017.
    Leiter2017a.pdf DOI: 10.1016/j.ifacol.2017.08.1467 Leiter2017a.bibtex
  49. M. Sharf and D. Zelazo, “A Network Optimization Approach to Cooperative Control Synthesis,” IEEE Control Systems Letters, 1(1):86–91, 2017.
    Sharf2017a_J.pdf DOI: 10.1109/lcsys.2017.2706948 Sharf2017a_J.bibtex
  50. D. Zelazo and M. Bürger, “On the Robustness of Uncertain Consensus Networks,” IEEE Transactions on Control of Network Systems, 4(2):170–178, 2017.
    Zelazo2014a_J.pdf DOI: 10.1109/tcns.2015.2485458 Zelazo2014a_J.bibtex
  51. Y. Ben Shoushan and D. Zelazo, “Negotiation Between Dynamical Systems with Connectivity Constraints,” in 57th Israel Annual Conference on Aerospace Sciences , Tel-Aviv, Israel, Feb. 2017.
    Shoushan2017.pdf Shoushan2017.bibtex
  52. Y. Ben-Shoushan, “Negotiation between Dynamical Systems with Connectivity Constraints,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2017.
    BenShoushan2017.pdf BenShoushan2017.bibtex
  53. D. Mukherjee and D. Zelazo, “Consensus Over Weighted Digraphs: A Robustness Perspective,” in 55th IEEE Conference on Decision and Control, Las Vegas, Nevada, Dec. 2016.
    Mukherjee2016b.pdf DOI: 10.1109/cdc.2016.7798784 Mukherjee2016b.bibtex
  54. D. Zelazo and M. Bürger, “On the Definiteness of the Weighted Laplacian and its Connection to Effective Resistance,” in 53rd IEEE Conference on Decision and Control, Los Angeles, CA, Dec. 2014.
    Zelazo2014f.pdf Zelazo2014f.slides DOI: 10.1109/cdc.2014.7039834 Zelazo2014f.bibtex
  55. M. Bürger, D. Zelazo, and F. Allgöwer, “On the Steady-State Inverse-Optimality of Passivity-Based Cooperative Control,” in 4th IFAC Workshop on Distributed Estimation and Control in Networked System, Koblenz, Germany, Sep. 2013.
    Mathias2013.pdf DOI: 10.3182/20130925-2-DE-4044.00004 Mathias2013.bibtex
  56. S. Schuler, D. Zelazo, and F. Allgöwer, “Robust Design of Sparse Relative Sensing Networks,” in European Control Conference, Zürich, Switzerland, Jul. 2013.
    Schuler2013.pdf Schuler2013.slides DOI: 10.23919/ecc.2013.6669618 Schuler2013.bibtex
  57. M. Bürger, D. Zelazo, and F. Allgöwer, “Hierarchical Clustering of Dynamical Networks Using a Saddle-Point Analysis,” IEEE Transactions on Automatic Control, 58(1):113–124, 2013.
    Burger2011_J.pdf DOI: 10.1109/TAC.2012.2206695 Burger2011_J.bibtex
  58. D. Zelazo, S. Schuler, and F. Allgöwer, “Performance and Design of Cycles in Consensus Networks,” Systems & Control Letters, 62(1):85–96, 2013.
    Zelazo2011_J.pdf DOI: 10.1016/j.sysconle.2012.10.014 Zelazo2011_J.bibtex
  59. S. Schuler, D. Zelazo, and F. Allgöwer, “Design of sparse relative sensing networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, Dec. 2012.
    Schuler2012.pdf Schuler2012.slides DOI: 10.1109/CDC.2012.6426358 Schuler2012.bibtex
  60. D. Zelazo and F. Allgöwer, “Eulerian Consensus Networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, Dec. 2012.
    Zelazo2012a.pdf Zelazo2012a.slides DOI: 10.1109/CDC.2012.6425921 Zelazo2012a.bibtex
  61. M. Bürger, D. Zelazo, and F. Allgöwer, “Combinatorial Insights and Robustness Analysis for Clustering in Dynamic Networks,” in American Control Conference, Montreal, Canada, Jul. 2012.
    Burger2012.pdf DOI: 10.1109/acc.2012.6314935 Burger2012.bibtex
  62. D. Zelazo, S. Schuler, and F. Allgöwer, “Cycles and Sparse Design of Consensus Networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, 2012.
    Zelazo2012d.pdf Zelazo2012d.slides DOI: 10.1109/cdc.2012.6426450 Zelazo2012d.bibtex
  63. B. Briegel, D. Zelazo, M. Bürger, and F. Allgöwer, “On the Zeros of Consensus Networks,” in 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, Dec. 2011.
    Briegel2011.pdf DOI: 10.1109/CDC.2011.6161047 Briegel2011.bibtex
  64. M. Bürger, D. Zelazo, and F. Allgöwer, “Network clustering: A dynamical systems and saddle-point perspective,” in 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, Dec. 2011.
    Zelazo2011b.pdf DOI: 10.1109/CDC.2011.6161045 Zelazo2011b.bibtex
  65. D. Zelazo, M. Bürger, and F. Allgöwer, “A Distributed Real-Time Algorithm for Preference-Based Agreement,” in Proc. 18th IFAC World Congress, Milan, Italy, Aug. 2011.
    Zelazo2011.pdf DOI: 10.3182/20110828-6-IT-1002.03155 Zelazo2011.bibtex
  66. D. Zelazo and M. Mesbahi, “Graph-Theoretic Analysis and Synthesis of Relative Sensing Networks,” IEEE Transactions on Automatic Control, 56(5):971–982, 2011.
    Zelazo2010_J.pdf DOI: 10.1109/TAC.2010.2085312 Zelazo2010_J.bibtex
  67. D. Zelazo and M. Mesbahi, “Edge Agreement: Graph-Theoretic Performance Bounds and Passivity Analysis,” IEEE Transactions on Automatic Control, 56(3):544–555, 2011.
    Zelazo2009b_J.pdf DOI: 10.1109/TAC.2010.2056730 Zelazo2009b_J.bibtex
  68. D. Zelazo and M. Mesbahi, “\mathcalH_∞ Performance and Robust Topology Design of Relative Sensing Networks,” in American Control Conference, Baltimore, MD, Jul. 2010.
    Zelazo2010b.pdf DOI: 10.1109/ACC.2010.5530963 Zelazo2010b.bibtex
  69. D. Zelazo and M. Mesbahi, “Graph-Theoretic Methods for Networked Dynamic Systems: Heterogeneity and H2 Performance,” in Efficient Modeling and Control of Large-Scale Systems, J. Mohammadpour and K. M. Grigoriadis, Eds. Boston, MA: Springer US, 2010, pp. 219–249.
    DOI: 10.1007/978-1-4419-5757-3 Zelazo.bibtex
  70. D. Zelazo and M. Mesbahi, “\mathcalH_2 Performance of Agreement Protocol with Noise: An Edge Based Approach,” in 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, China, Dec. 2009.
    Zelazo2009b.pdf DOI: 10.1109/CDC.2009.5400513 Zelazo2009b.bibtex
  71. D. Zelazo and M. Mesbahi, “\mathcalH_2 Analysis and Synthesis of Networked Dynamic Systems,” in American Control Conference, St. Louis, MO, Jun. 2009.
    Zelazo2009.pdf DOI: 10.1109/ACC.2009.5160153 Zelazo2009.bibtex
  72. D. Zelazo and M. Mesbahi, “\mathcalH_2 Performance of Relative Sensing Networks: Analysis and Synthesis,” in AIAA Infotech@Aerospace Conference and AIAA Unmanned ...Unlimited Conference, Seattle, WA, Apr. 2009, no. 7.
    Zelazo2009a.pdf DOI: 10.2514/6.2009-1840 Zelazo2009a.bibtex
  73. D. Zelazo, “Graph-theoretic Methods for the Analysis and Synthesis of Networked Dynamic Systems,” phdthesis, University of Washington, Department of Aeronautics & Astronautics, 2009.
    Zelazo2010.pdf Zelazo2010.bibtex
  74. D. Zelazo, A. Rahmani, J. Sandhu, and M. Mesbahi, “Decentralized Formation Control via the Edge Laplacian,” in American Control Conference, Seattle, WA, Jun. 2008.
    Zelazo2008a.pdf DOI: 10.1109/ACC.2008.4586588 Zelazo2008a.bibtex