SIAM Team (Signal, Image, AutoMatic) is an interdisciplinary team whose research revolves around the four essential steps in the general study of a system: perception, observation, modeling, and control. The two systems targeted for applying these methods are mainly vehicles and biological systems. The non-standard behavior of the systems studied, as well as the consideration of their autonomy, led the team’s researchers to implement original methods on very varied aspects: behavior at the safety limit for vehicles, air vehicles lighter than air, cooperation of autonomous vehicles, mobility of cells in the ameboid phase and consideration of the micro-environment.

This expertise contributes to the definition of the team and its influence. Based on heterogeneous applications, the team has imagined methods that cut across the usual tools, thus offering the possibility of linking disciplines and, therefore, communities that are not naturally linked. Let us cite a few examples: the decomposition of statistical tensors for the analysis of 3D images, the theory of statistical decision applied to the recognition of cell types, the consideration of deformable models with aero-elastic forces to control airships, the classification of the constrained stability of non-Hamiltonian systems.

The team, therefore, relies on these multidisciplinary interactions to establish its research project in a favorable local context (installation of the CHSF in 2010, of the IRBA in 2009, extension of Genopole, and rapprochement with IFFSTAR-LIVIC).

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Sliding mode control Linear Matrix Inequality LMI Deep learning Gait rehabilitation Consensus control Six-rotor unmanned aerial vehicles LBP LMI Object detection Segmentation Clinical gait analysis Protocols Networked control systems Linear matrix inequalities Actuator saturation Stability analysis Fault estimation Linear matrix inequality Ridge extraction Multi-agent systems Cameras State estimation Apprentissage profond Nonlinear system Optimization Nonlinear control Optimal control Calibration Unknown input Fault-tolerant control Model Predictive Control Disturbance observer Interval observer Robust control Pooling function Fault tolerant control Lyapunov methods PID control Fuzzy systems Renewable source integration Heuristic algorithms Linear systems Costs Roads UAV Structure from Motion Machine learning Distributed control Adaptive systems Reinforcement learning Estimation Fault-Tolerant Control Takagi-Sugeno Vehicle dynamics Deep Learning Neural Networks Linear matrix inequalities LMIs Cyberattack Topology Stability Autonomous Vehicles Optical flow Sensor faults Neural networks Actuators Home Electrical Appliances HEAs Rank aggregation Grid stability Event-triggered mechanism Nonlinear systems Autonomous driving Time-frequency Fuzzy control Localization Adaptive fuzzy control Semantic segmentation Multi-task learning Synchrosqueezing Lyapunov Theory Convolutional neural networks Games LMI constraints Adaptation models Observer-based control LPV systems Time delay Fuzzy logic systems Contour extraction Markov processes Fault detection UAVs Adaptive control Photovoltaic system DC MicroGrids Autonomous aerial vehicles Stability criteria Model predictive control Observers LMIs Identification