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My main research interests are primarily focused on the structural analysis and the control of dynamical networks.

Complex interconnections of interacting agents, each with its own dynamics, are ubiquitous in our daily life. We are part of social and economic networks, we use technological networks (power, transportation, telecommunication, computer networks), our organisms rely on extremely complex interactions of DNA, proteins and biomolecules (biochemical reaction networks, gene regulatory and metabolic networks, signalling pathways). Multi-agent robotics is also arising, aimed at employing swarms of robots, drones or unmanned aerial vehicles to perform critical collaborative tasks (cooperative manipulation and transportation, including human-robot interaction; patrolling; search and rescue).

Each of these systems can be modelled as a dynamical network: a complex dynamical system endowed with a network structure, composed of several dynamical sub-units that are interconnected according to a (possibly time-varying) network topology. This general class of models embraces natural and engineered complex systems, and is thus relevant in systems biology, social networks, ICT, autonomous systems and multi-agent robotics.

Such a broad scope is well suited to system and control theory, a discipline that is naturally fit for crossing borders and putting mathematics into action in the real world, to understand it (explain and unravel the essence of natural behaviours) and to govern it (by designing controllers that enforce suitable behaviours). The mathematical exactness of system theory can not only streamline technological progress, but also help us gain a deeper insight into the complex, fascinating and apparently haphazard phenomena occurring in chemical reactors, living cells and organisms.

We propose dynamical networks as an inclusive modelling framework for describing complex and potentially heterogeneous interconnected systems, and combine system-theoretical, control-theoretical and graph theoretical tools to achieve results on the analysis and the control of dynamical networks.

A twofold goal, a unified framework: local interactions, global behaviour.

When we manage complex large-scale engineered systems, our aim is to control or coordinate the overall system so as to achieve the desired global behaviour, even though we have limited local information and we can enforce local actions only. When we analyse natural and biological systems, we are interested in understanding how the local interactions can give rise to a global behaviour that is often astoundingly robust to environmental changes and perturbations.

Structural (parameter-free) methods are particularly useful to deal with systems whose parameter values (and functional expressions, due to modelling choices) are varying, uncertain or unknown. Can a class of systems necessarily give rise to a particular qualitative behaviour, regardless of specific parameter values? Quite surprisingly, this is indeed the case for many natural systems: this reveals how the design principles selected by evolution have rooted specific qualitative behaviours (associated with specific motifs) in the system structure, allowing living cells and organs to robustly perform their task in spite of severe uncertainties, noise and environmental fluctuations.

If we unravel the structural paradigms that guarantee robustness and resilience in complex natural networks, we can then apply them to engineered systems and develop biologically-inspired frameworks to design efficient large-scale networks. As nature often adopts distributed strategies, so distributed approaches are fundamental when dealing with complex engineered networks. Distributed optimisation and estimation algorithms are fundamental in sensor networks, localisation problems, synchronisation and coordination of autonomous agents. The decentralised control of dynamical networks is crucial for applications spanning from traffic congestion problems, supply chains and inventory management to water and energy distribution, formation control and collision avoidance, coordination of robots and autonomous vehicles, power networks and smart grids, telecommunications, computer and mobile phone networks. These distributed strategies need to robustly face delays, saturations, topology changes, failures and unpredictable events.

We aim at investigating the underlying structure and topology-based properties of dynamical networks in various contexts. In particular, the research goal is twofold:

1) explain how dynamical networks in nature can ensure a global behaviour that arises from the complex interplay of local interactions and exhibits an extraordinary robustness to parameter variations and uncertainty;

2) devise robust control strategies for dynamical networks in engineering, which are able to enforce the desired global behaviour by means of the local actions of controllers that decide their strategy based on local information only.

On the one hand, we want to identify properties that pertain to a whole class of dynamical networks, due to its inherent structure (interconnection topology, which describes how the global behaviour results from local interactions); on the other hand, we want to design network-decentralised control and estimation strategies for dynamical networks that enforce a global behaviour through local actions.

These two aspects are strongly connected: structural analysis aims at establishing why a given structure is able to produce a certain global behaviour, while network-decentralised control aims at establishing which behaviours can be enforced on a structure. Addressing both research goals within a unified theoretical framework allows us to better understand how to deal with complexity and heterogeneity in both biological and man-made systems: in particular, it can help us both learn from nature (i.e., develop bio-inspired design strategies for control and robotics, thus improving the efficiency and the resilience of artificial systems) and engineer nature (i.e., streamline synthetic biology and the design of artificial biomolecular circuits, with the same systematic bottom-up approach that is used for building complex artificial systems, thus allowing for innovative biotechnologies and drugs able to improve human health and quality of life).