The MODESTY team focuses its research on the study of large-scale interacting systems. These systems are classified as large either due to the high number of subsystems they comprise (such as multi-agent systems or interconnected nonlinear systems) or because they represent infinite-dimensional dynamics, such as partial differential equations or time-delay systems, potentially interconnected in networks.
The team’s work aims to develop new methods that account for the specificities of large-scale systems, addressing the classical challenges of control theory: identification, observation, stability analysis, and stabilization. To achieve this, the team proposes structural analysis methods tailored to networked systems, enabling the synthesis of robust, low-complexity control laws based on output feedback. These control laws rely on the design of observers capable of reconstructing dynamics in real time and estimating unknown parameters, even in the presence of uncertainties. This research aligns with data-driven model learning. Additionally, the team develops multi-scale methods, specifically designed for multi-agent systems, to address challenges such as synchronization and consensus.
The techniques developed by the MODESTY team have applications in many fields, including the stabilization of mechanical vibrations in drilling, traffic flow analysis and estimation, and applications in life sciences. These efforts demonstrate the tangible impact of its research on major industrial and societal challenges.
The research conducted by the MODESTY team focuses on real-time state estimation of dynamic systems, whether they are nonlinear or infinite-dimensional linear systems. The techniques developed by the team for observer synthesisincorporate the estimation of unknown parameters and the design of robust estimation methods, capable of accounting for uncertainties, noise, and disturbances. These approaches not only enable a detailed understanding of underlying dynamics but also facilitate learning and refining models based on measured data.
Methods: Interval observation, KKL observers, adaptive observers, finite-time observers, pole placement methods, learning-based methods.
The MODESTY team investigates stability, asymptotic stabilization, and control of networked interconnected systems. These systems may consist of interconnected Partial Differential Equations or nonlinear dynamics. The team’s research focuses on designing output-feedback control laws to achieve robust regulation, capable of handling parametric uncertainties and disturbances. Additionally, we employ Lagrangian approaches to analyze stability using Lyapunov functions, while preserving the physical properties of the systems.
Methods: Internal model-based control, backstepping, time-delay systems approaches, pole placement methods, mean-field control.
The MODESTY team also explores the fundamental challenges of consensus and synchronization in multi-agent systems and graphons, where the goal is to ensure coordination and alignment of behaviors or states among interconnected agents. These mechanisms are critical for applications ranging from sensor networks to robot swarms. The team’s work enables the study of convergence toward synchronized states, even in dynamic or uncertain environments, while ensuring the scalability and robustness of proposed solutions. This research contributes to redefining decentralized coordination in complex systems, where network structure and agent interactions play a key role.
Methods: graphon theory, nonlinear synchronization, consensus protocols, flocking methods.
Thanks to their versatility, the methods and tools developed by the MODESTY team have applications across a wide range of fields, including bioprocess systems, aeronautical and space systems, and telecommunications. Over the past few years, the team’s research has particularly focused on three major application areas.
The MODESTY team makes significant contributions to the biomedical field, particularly through the estimation of anaerobic digestion systems, the regulation of microalgae populations, and the estimation of neural dynamics. Among its advancements, the team has also developed innovative control techniques for optimizing artificial ventilation in intensive care patients. These efforts highlight the tangible impact of its research on critical health challenges.
The MODESTY team focuses on modeling and estimating road traffic by leveraging macroscopic methods based on distributed approaches. These advancements enable the development of more effective control strategies, aimed at reducing traffic congestion, facilitating the deployment of autonomous vehicles, and optimizing the energy management of electric vehicles.
The research conducted by the MODESTY team has refined the estimation of mechanical vibrations in drilling systems, paving the way for more effective adaptive control laws. These advancements also extend to real-time state estimation in geothermal production systems, thereby highlighting the significant contribution of the team’s methods to the energy sector.
Researcher – CNRS
Automatique et systèmes – MODESTY
jean.auriol@l2s.centralesupelec.fr
Bât. Breguet .
