Nature provides us with several examples of collective systems or multi-agent systems (e.g., fish schools or bird flocks) that can achieve desired goals through appropriate strategies that promote self-organization. With the development of programming techniques and computational capabilities, control design also needs to address control and optimization problems of increasing scale and complexity, exhibiting self-organizing properties inspired by or mimicking natural collective systems. When dealing with uncertain collective systems, not only is there a lack of a central unit that knows the entire system state, but there is also a lack of knowledge about system dynamics. In this situation, local information must be used for control and estimation, for example, through distributed learning mechanisms to compensate for the lack of complete and global information.

Representative Topics

• Distributed learning
• Distributed estimation and distributed adaptation
• Adaptive multi-agent systems
• Uncertain collective systems

Representative Applications

• Human-machine interaction systems
• Adaptive formation (e.g., unmanned vehicles)
• Human-in-the-loop control
• Social artificial intelligence

Representative Publications

• Baldi S., Azzollini I. A., and Ioannou P. A., “A distributed indirect adaptive approach to cooperative tracking in networks of uncertain single-input single-output systems”, IEEE Transactions on Automatic Control, Vol. 66(10), pp. 4844-4851, 2021. doi:10.1109/TAC.2020.3038742
• Azzollini I. A., Yuan S., Yu W., and Baldi S., “Adaptive leader-follower synchronization over heterogeneous and uncertain networks of linear systems without distributed observer”, IEEE Transactions on Automatic Control, Vol. 66(4), pp. 1925-1931, 2021. doi:10.1109/TAC.2020.3000195
• Baldi S. and Frasca P., “Leaderless synchronization of heterogeneous oscillators by adaptively learning the group model”. IEEE Transactions on Automatic Control, Vol. 65(1), pp. 412-418, 2020. doi:10.1109/TAC.2019.2914664