Michel Perez

Papers

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About

Michel Perez is a leading researcher in distributed control and multi-agent coordination, with a focus on feedback optimisation—a cutting-edge technique that steers robotic systems toward optimal steady states without centralised oversight. His most-cited work, "Distributed Feedback Optimisation for Robotic Coordination" (2022, 6 citations), introduces a novel framework that enables robots to converge asymptotically to optimal configurations through local interactions alone, proving both theoretical convergence and practical applicability. This contribution bridges control theory and distributed optimisation, offering scalable solutions for swarm robotics, sensor networks, and autonomous systems. Perez’s research has significant implications for real-world coordination problems, from environmental monitoring to industrial automation. His work is gaining traction among control engineers and roboticists, with citations reflecting its growing influence in the field. By demonstrating that feedback optimisation can be decentralised, Perez has opened new pathways for resilient, efficient multi-agent systems. His achievements highlight a commitment to rigorous mathematical foundations and practical deployment, making him a rising voice in modern control theory and robotic coordination.