Saverio Bolognani

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About

Saverio Bolognani is a leading researcher at the intersection of control theory, game theory, and optimization for multi-agent and cyber-physical systems. His work focuses on designing scalable, distributed algorithms that enable complex networks—from robotic swarms to power grids—to operate efficiently and autonomously. A key contribution is his framework for **sampled-data online feedback equilibrium seeking**, which provides rigorous stability and tracking guarantees for systems that must optimize their performance in real time, even as operating conditions shift. This work, with 27 citations, bridges the gap between online optimization and closed-loop control. To address the challenge of coordinating agents with conflicting objectives, Bolognani introduced **Posetal Games**, a novel game-theoretic model where agents prioritize multiple, often competing metrics using partially ordered sets. This framework, cited 12 times, allows for the design of equilibria that respect hierarchical rules, a critical capability for autonomous systems operating under safety constraints. More recently, he has tackled the curse of dimensionality in **dynamic games** by developing a factorization process that exploits spatio-temporal independence among players, making these models tractable for large-scale applications. His work is distinguished by its mathematical rigor and its direct relevance to the next generation of autonomous and networked systems.