CONTROL OF COMPLEX MULTIAGENT SYSTEMS

In a recent book chapter, we review the main achivements and challenges in the control of complex network systems. For more details, see

• Zino L., Barzel B., Rizzo A., Network Science and Automation in Handbook of Automation, 2nd ed. (ed. S.Y. Nof), pp. 251–274, Springer, 2023.

 

We studied evolutionary dynamics on networks and designed a feedback control policy to achieve fast spread:

• Zino L., Como G., and Fagnani F., Fast Diffusion of a Mutant in Controlled Evolutionary Dynamics. Proceedings of the 20th IFAC World Congress, Jul 2017, Toulouse, France

• Zino L., Como G., and Fagnani F., Controlling Evolutionary Dynamics in Networks: A Case Study. Proceedings of the 7th IFAC Workshop on Distributed Estimation and Control in Networked Systems, Aug 2018, Groningen, The Netherlands
  Finalist of the Young Author Award.

• Zino L., Como G., and Fagnani F., Fast Spread in Controlled Evolutionary Dynamics. IEEE Transactions on Control of Network Systems, 10(3), pp. 1555-1567, 2023

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We studied different schemes to control complex dynamics, including a case-study on multi-agent systems by means of a game-theoretic mechanism:

• Pagone M., Zino L., and Novara C., A Pontryagin-based Game-theoretic Approach for Robust Nonlinear
  Model Predictive Control. Proc. 62nd IEEE CDC, 2023. 

• Marinello G., Zino L., Novara C., and Pagone M., A Game-theoretic Approach to Cooperative Robust Nonlinear Model Predictive Control for a Network of Unmanned Ground Vehicles. To appear in the proceedings of the 11th CoDIT, 2025

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We have developed a theory on adaptive gain control for game-theoretic dynamics:

• Zino L., Ye M., Rizzo A., and Calafiore G.C., On Adaptive-Gain Control of Replicator Dynamics in Population Games. Proc. 62nd IEEE CDC, 2023.

• Zino L., Ye M., Calafiore G.C., and Rizzo A., Equilibrium Selection in Replicator Equations Using Adaptive-Gain Control. Published online in the IEEE Transactions on Automatic Control, 2025. 

• Gavin R., Paarporn K., Ye M., Zino L., and Cao M., Adaptive-Gain Control for Equilibrium Selection in the Logit Dynamics, To appear in the proceedings of the 64th Conference on Decision and Control, 2025.

   

 

We studied controllability of networked multi-agent systems:

• Wang Q., Diao W., Zino L., Peng X., and Zhong W., Observer-Based Secure Event-Triggered Bipartite Consensus Control of Linear Multiagent Systems Subject to Denial-of-Service Attacks. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(12), 5054-5058, 2022

• Wang Q., Zino L., Tan D., Xu J., and Zhong W., Fully Distributed Quantized Secure Bipartite Consensus Control of Nonlinear Multiagent Systems Subject to Denial-of-Service Attacks. Neurocomputing, 505, 101–115, 2022

• Wang Q., He W., Zino L., Tan D., and Zhong W., Bipartite Consensus for a Class of Nonlinear Multi-agent
  Systems Under Switching Topologies: A Disturbance Observer-Based Approach. Neurocomputing, 488, 130–143, 2022

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Determining the positions of a set of networked agents in a distributed manner has become an important problem, as it plays an increasingly important role in multiple applications. We deal with this problem in:

• Wu J., Zino L, Lin Z., and Rizzo A., Distributed Finite-Time Cooperative Localization for Three-Dimensional Sensor Networks. IEEE Transactions on Networking, 2025

• Wu J., Zino L, Lin Z., and Rizzo A., Simultaneous Robot and Feature Localization via Angle-Based Sensing with Barycentric Coordinate Representation, 2025 (Under Review)

• Wu J., Feng R., Lin Z., Zino L., and Rizzo A., DBMR-Loc: A Distributed Sensor Localization Framework for Noisy Distance Measurements, 2025 (Under Review)