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Séminaire MoFED – 25 novembre 2025 – 14h – Saint-Jérôme

Alessandro Giua, DIEE, Univ. of Cagliari, Italy

Title : State estimation of partially observed discrete event systems under attack

Abstract : Partially observed discrete event systems are a general formalism dating back to the definition of nondeterministic automata. The assumption is that the sequence of events generated by a plant is observed through a mask, so that an agent observing the plant may have incomplete information concerning its evolution and, correspondingly, the past state trajectory and the current state.
The objective of this talk is to describe the basic principles of partially observable discrete event systems showing how the state estimation problem can be addressed for systems subject to cyber-attacks. An operator receives the sensor readings produced by a plant through a communication channel and uses this information to estimate the current state of the plant. The observation may be corrupted by an attacker which can insert and erase some sensor readings with the aim of altering the state estimation of the operator. Furthermore, the attacker wants to remain stealthy, namely the operator should not realize that its observation has been corrupted.
I will show how to determine an automaton, called joint estimator under attack, that describes for each possible observation produced by the plant and for each possible attack, what is the state estimation computed by the operator. Such a structure is obtained by the concurrent composition of two state observers, called attacker observer and operator observer. The joint estimator can be used to determine if there exists a stealthy harmful attack function such that the set of states consistent with the uncorrupted observation computed by the attacker, and the set of states consistent with the corrupted observation computed by the observer, satisfy a given relation.

Biography : Alessandro Giua received a Laurea degree in electrical engineering from the University of Cagliari, Italy, in 1988 and master’s and Ph.D. degrees in computer and systems engineering from the Rensselaer Polytechnic Institute, Troy, NY, USA, in 1990 and 1992, respectively. He is currently Professor of Automatic Control with the Department of Electrical and Electronic Engineering of the University of Cagliari.  His research interests include discrete-event systems, hybrid systems, networked control systems, Petri nets and failure diagnosis. He has served as Editor-in-Chief of the IFAC journal Nonlinear Analysis: Hybrid Systems, Senior Editor of the IEEE Trans. on Automatic Control, and Department Editor of the journal Discrete Event Dynamic Systems. He is a Fellow of the IEEE and a Fellow of the IFAC for contributions to Discrete-Event and Hybrid Systems. He is a recipient of the People’s Republic of China Friendship Award.

Cristian Mahulea, Departamento de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Spain

Title: Efficient Path Planning and Task Allocation for Large Robotic Teams Using Petri Net Structural Properties

Abstract: Coordinating large teams of mobile robots in complex environments remains challenging due to the combinatorial explosion of possible assignments and the need for safe, collision-free motion. This talk presents a new approach that models robot motion and global task specifications using Robot Motion Petri Nets and exploits the total unimodularity of the resulting constraint matrices. By proving this structural property, we relax classically NP-hard integer formulations into tractable linear programs while preserving integrality, enabling solutions that scale to thousands of robots. A two-stage algorithm combines fast LP relaxations with the automatic insertion of minimal synchronization points to guarantee collision avoidance. Comparative evaluations on standard MAPF benchmarks demonstrate significant reductions in computational time and improved scalability over traditional ILP-based or graph-search methods. Applications range from industrial logistics to smart manufacturing and autonomous fleets.

Biography: Cristian Mahulea is Full Professor of Automatic Control at the University of Zaragoza, Spain. He received his M.Sc. in Control Engineering from the “Gheorghe Asachi” Technical University of Iași (Romania) and his Ph.D. in Systems Engineering from the University of Zaragoza. His research focuses on discrete event and hybrid systems, Petri nets, and the planning and control of multi-robot systems, with applications in logistics and healthcare. He serves as Associate Editor of the IEEE Transactions on Automatic Control (TAC), the International Journal of Robotics Research (IJRR), and the Journal of Discrete Event Dynamic Systems (JDES), and has served on editorial boards of several leading journals. Prof. Mahulea has co-authored two books and organized major international conferences in automation.

Yan Monier, LURPA, ENS Paris-Saclay

Title:  ADAM: Anomaly Detection by Adaptive Modeling

Abstract: This talk presents ADAM, a project under development that explores a new approach to creating and updating interpretable digital twins for industrial production. Unlike opaque “black-box” AI or costly, heavily hand-crafted models, ADAM leverages existing production data to automatically build and refine digital twins with minimal supervision. It integrates models from discrete event systems, such as automata and Petri nets, while also taking into account time, flows, and dynamic equations to capture the full hybrid behavior of industrial processes. Beyond its methodological advances, ADAM serves as a way to concatenate and operationalize years of research conducted across multiple laboratories into a concrete, real-world problem solver for the industrial field, rather than a purely academic exercise. Its goal is to detect anomalies, failures, and cyberattacks at an early stage while ensuring that the models remain transparent and understandable to operators. Currently under construction and backed by the University of Paris-Saclay, the project aims to validate its potential through pilot collaborations, reduce downtime in real industrial environments, and ultimately contribute to a more resilient, secure, and future-ready manufacturing industry.

Biography: Yan Monier recently completed a PhD in Automation and Control at ENS Paris-Saclay, where he developed methods for hybrid model synthesis that combine physical modeling with data-driven analysis. His recent research included work on cyberattack and anomaly detection in cyberphysical systems and led to the development of software tools applying these methods to realistic laboratory-scale production processes. Focused on making complex industrial systems easier to monitor, predict, and control, Yan now continues this line of research through ADAM, a project that brings interpretable digital twins into real-world production. Passionate about resilience and sustainability, he aims to reduce downtime, strengthen cybersecurity, and enable industries to modernize their processes without costly equipment replacement.

Séminaire jeunes chercheurs Diapro – 21 novembre 2024 – Saint-Jérôme

• Lien vers la vidéo du séminaire (mot de passe : Diapro) : https://amupod.univ-amu.fr/video/33442-seminaire-diapro-21-11-2024/
• Lien vers les présentations : https://sync.lis-lab.fr/index.php/s/EgC2nCWpZaFz383

William Jussiau, jeune docteur de l’ONERA

Titre : Lois de commande pour le contrôle des écoulements oscillateurs

Résumé : Cette thèse porte sur la synthèse de lois de commande pour les écoulements oscillateurs à faible nombre de Reynolds. Nous y étudions deux configurations canoniques en 2D : l’écoulement autour d’un cylindre, et l’écoulement au-dessus d’une cavité ouverte. Ces deux cas d’étude présentent un équilibre stationnaire instable, et un régime d’oscillations auto-entretenues – respectivement, un cycle limite et un attracteur torique. L’objectif principal est la synthèse de lois de commande pour supprimer complètement le régime oscillatoire, pour réduire la traînée moyenne, les vibrations structurelles ou le rayonnement acoustique tonal. En pratique, la synthèse de lois de commande pour ces systèmes est rendue difficile par la diversité des phénomènes dynamiques émergeant des équations de Navier-Stokes, non-linéaires et de dimension infinie. Nous proposons trois méthodes distinctes pour réaliser cette tâche, utilisant respectivement une paramétrisation des correcteurs stabilisants, la continuation numérique et le formalisme de la résolvante moyenne.

Soha KANSO, doctorante, CRAN, Université de Lorraine, Nancy. ATER Polytech Nancy

Titre : Safe Reinforcement Learning and Degradation Tolerant Control Design

Résumé : Safety-critical dynamical systems are essential in various industries, such as aerospace domain, autonomous systems, robots in healthcare area etc., where violating safety constraints and structural or functional failure may lead to catastrophic consequences. A significant challenge in these systems is the degradation of components and actuators, which can compromise safety and stability of systems. As such, incorporating system’s health state within the control design framework is essential to ensure tolerance to functional degradation. Moreover, such system models often involve uncertainties and incomplete knowledge, especially as components degrade, altering system dynamics in a nonlinear manner. This underscores the necessity for the development of learning approaches that incorporate the available data within the control learning paradigm.
In this context, Reinforcement Learning (RL) emerges as a powerful approach, capable of learning optimal control laws for partially or fully unknown dynamic systems, in the presence of input-output data (without the exact knowledge of system models). However, a major challenge in applying RL methods to safety-critical systems lies in ensuring safety during both the exploration and exploitation phases. Exploration involves introducing probing noise to the policy in order to collect informative data across the state space, while exploitation refers to applying the learned policy to optimize performance in real operations.

To this end, this presentation will first explore an off-policy safe RL approach for the regulation and the tracking problem in continuous-time nonlinear systems affine in control input. A novel approach will be presented that ensures system stability and safety during all phases: initialization, exploration, and exploitation. By using quadratic programming with control Lyapunov function (CLF) and control barrier function (CBF), the proposed approach ensures stability and safety of the system during initialization and exploration phases. Furthermore, during exploitation, the safety of the learned policy is ensured by augmenting the cost function with reciprocal CBFs, thus balancing performance optimization and safety. The second part of the talk will focus on addressing actuator degradation, which poses a critical threat to system performance and stability. A degradation-tolerant controller based on RL is introduced for continuous-time nonlinear systems affine in control input. The objectives are twofold: ensuring system stability despite degradation, and decelerating the degradation rate to complete missions and extend actuator life. This is achieved by imposing constraints on degradation rates using CBFs. Furthermore, a cyclic off-policy algorithm is presented, enabling iterative exploration and exploitation across multiple learning cycles. This allows for continuous updates of neural network weights with recent information on degradation levels, ensuring that the learned policy effectively stabilizes the system while accounting for degradation effects.

In the developed approaches, neural networks are used to approximate both the value function and the control policy, thereby enabling efficient learning. Simulation results will be presented to demonstrate the efficiency of the proposed approaches.

Vendredi 8 novembre 9h, Saint-Jérôme

Anes Lazri, L2S, Université Paris Saclay, lien Scholar

Titre : Analyse et contrôle de la synchronisation dans les réseaux de systèmes complexes

Résumé :
Les réseaux complexes sont omniprésents dans notre quotidien, qu’il
s’agisse de réseaux électriques, de réseaux sociaux ou encore de
réseaux biologiques. Ces réseaux interconnectent de nombreux systèmes
individuels, créant ainsi des interactions qui influencent leur
comportement collectif. En plus de la dynamique propre à chaque
système, une dynamique collective émerge, donnant lieu à des
phénomènes tels que la synchronisation ou le regroupement en clusters.

Lors de ce séminaire, nous aborderons deux aspects majeurs influençant
la synchronisation : la force des interconnexions et la topologie du
réseau. Nous explorerons comment ces facteurs impactent la dynamique
des réseaux de systèmes complexes, notamment dans des scénarios où :

• Le réseau est partitionné en plusieurs groupes capables de communiquer entre eux, permettant à ces groupes de former des « macro-noeuds » qui peuvent atteindre un comportement commun ou un consensus ;
• Les couplages entre nœuds ne sont pas suffisamment forts pour garantir une synchronisation complète, entraînant l’émergence de clusters distincts au sein du réseau.

Nous examinerons ces phénomènes avec un certain degré de
généralité, mais aussi à travers des exemples concrets tels que les
réseaux d’oscillateurs. Le but de cette présentation est de discuter
des algorithmes de contrôle et des outils d’analyse permettant
d’assurer la synchronisation et le consensus dans ces réseaux. Nous
mettrons également en lumière des cas particuliers où le
multi-consensus émerge, et nous analyserons différents types de
topologies et de connexions dans ce contexte.

Salim Zekraoui, LAGEPP, Université Lyon 1, lien Scholar

Titre : Finite-time control of LTI/PDE systems with input delay using a PDE-based approach.

Abstract :
Time-delay systems are ubiquitous in control engineering. As time delays may cause performance degradation or instability of the closed-loop system, control design becomes a central issue; however, due to the infinite-dimensional nature of those systems, control continues to be challenging. Moreover, in many applications, like rendezvous and missile guidance, the transient process must occur within a given time while also managing the effect of the delay. The need to meet these time constraints and to increase temporal performance has motivated non-asymptotic stabilization (stabilization + convergence in finite time). In this talk, we will focus on the non-asymptotic stabilization of some classes of infinite-dimensional systems, namely LTI systems with input delays and reaction-diffusion PDEs with boundary input delays, utilizing a PDE backstepping-based approach. The approach consists mainly of rewriting the initial delayed LTI/PDE system as an ODE/PDE-PDE cascade system; and then transforming the resulting cascaded system, using an invertible transformation, into a well-chosen non-asymptotically stable target system. We show that the inverse transformation transfers the non-asymptotic stability property back to the initial ODE/PDE-PDE cascade system. 

In addition, we consider the problem of boundary state-dependent finite/fixed-time stabilization of reaction-diffusion PDEs. To the best of our knowledge, this problem has remained open in the literature for a considerable long time. We tackled this challenging problem using classical methods related to Control Lyapunov functions.