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 Sampled-Data Consensus: Divide and Concur,” IEEE Control Systems Letters, 6:343–348, 2022.
    Barkai2022a_J.pdf DOI: 10.1109/lcsys.2021.3074589
  2. 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
  3. 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
  4. 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
  5. 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
  6. D. Zelazo and F. Allgöwer, “Eulerian Consensus Networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, Dec. 2012.
    Zelazo2012a.pdf DOI: 10.1109/CDC.2012.6425921
  7. 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
  8. 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

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
  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
  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
  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

All our publications in this area can be found below:

Related Publications:

  1. F. Yue and D. Zelazo, “A Passivity Analysis for Nonlinear Consensus on Balanced Digraphs,” 2024.
    Yue2024_ECC.pdf arXiv: https://arxiv.org/abs/2411.05933
  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
  3. 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.
  4. 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.slides
  5. 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.slides
  6. 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.slides
  7. 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.
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. M. Sharf and D. Zelazo, “Cluster assignment in multi-agent systems: Sparsity bounds and fault tolerance,” Asian Journal of Control, 2023.
    Sharf2023b_J.pdf DOI: 10.1002/asjc.3149
  15. 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
  16. 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.slides
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. N. Leiter, “Graph-based Model Reduction Methods for Multi-Agent Systems,” phdthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2022.
    Leiter2022.pdf
  23. A. Priel, “Consensus Kalman Filtering: Filter Design and Event-Triggering,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2022.
    Priel2022.pdf
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. M. Sharf, “Network Optimization Methods in Passivity-Based Cooperative Control,” phdthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2020.
    Sharf2020.pdf
  30. Y. Palti, “Deployment Strategies for Coverage Control Problems,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2020.
    Palti2020.pdf
  31. D. Muhkerjee 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
  32. D. Muhkerjee 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
  33. 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
  34. 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
  35. 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
  36. 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 DOI: 10.1109/lcsys.2019.2914128
  37. 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
  38. 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.
  39. D. Mukherjee and D. Zelazo, “Robust Consensus of Higher Order Agents over Cycle Graphs,” in 58th Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Feb. 2018.
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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.
  45. 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
  46. Y. Ben-Shoushan, “Negotiation between Dynamical Systems with Connectivity Constraints,” mastersthesis, Technion - Israel Institute of Technology, Aerospace Engineering Department, 2017.
    BenShoushan2017.pdf
  47. D. Mukherjee and D. Zelazo, “Robust Consensus of Higher Order Agents over Cycle Graphs,” in 56th Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Mar. 2016.
    Mukherjee2016a.slides
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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 DOI: 10.1109/CDC.2012.6426358
  56. D. Zelazo and F. Allgöwer, “Eulerian Consensus Networks,” in 51st IEEE Conference on Decision and Control, Maui, HI, Dec. 2012.
    Zelazo2012a.pdf DOI: 10.1109/CDC.2012.6425921
  57. 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 DOI: 10.1109/cdc.2012.6426450
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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