Nicolas Boizot

Centre National de la Recherche Scientifique, Laboratoire d’Informatique et Systèmes

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About

Nicolas Boizot is a researcher whose work sits at the intersection of nonlinear control theory, optimal energy management, and underwater robotics. His primary contributions lie in advancing state estimation for complex dynamical systems and developing practical models for tethered robotic platforms. In his highly cited 2018 paper (16 citations), Boizot introduced a high-gain extended Kalman filter for continuous-discrete systems with asynchronous measurements, proving global exponential convergence—a significant step forward for observers operating under challenging multirate sampling conditions. More recently, his 2024 work on an augmented catenary model for underwater tethered robots (4 citations) addresses the critical influence of hydrodynamic damping on cable dynamics, offering a novel modeling framework that improves the fidelity and control of tethered subsea vehicles. Boizot has also tackled optimal control problems with non-differentiable cost functions, as in his 2020 study on consumption minimization for vehicle models, demonstrating a versatility that spans from theoretical observer design to applied energy-aware robotics. His research is particularly relevant for engineers developing autonomous systems that must operate reliably in real-world, sensor-limited environments.