Mathias Hudoba de Badyn

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About

Mathias Hudoba de Badyn is a rising leader at the intersection of control theory, optimization, and multi-agent robotics. His research centers on developing rigorous, scalable frameworks for coordinating complex dynamical systems, with a particular focus on feedback optimization and distributed decision-making. In his most cited work, "Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking" (27 citations), he introduced a general framework for constructing feedback controllers that drive systems to efficient, time-varying operating points—a foundational contribution to online optimization in control. He has also advanced autonomous exploration with "Decentralized Trajectory Optimization for Multi-Agent Ergodic Exploration" (10 citations), pioneering decentralized ergodic planning that enables robot teams to efficiently cover areas proportional to information density. His 2022 paper on "Distributed Feedback Optimisation for Robotic Coordination" (6 citations) further demonstrates how to achieve optimal steady-state configurations in a distributed manner, proving asymptotic convergence. Through these works, Hudoba de Badyn has established himself as a key figure in bridging feedback control and optimization, with direct applications to robotics, smart grids, and networked systems.