Synthèse d'algorithmes d'optimisation en-ligne par commande extrémale

Optimisation en temps réel a base de modèles de connaissance -- Optimisation en temps réel a base de modèles empiriques -- Dépendance de l'erreur sur la solution optimale des méthodes de perturbations a la fréquence d'excitation -- Accélération de la convergence -- Amélioration des propriétés de convergence de la commande extrémale par la méthode des perturbations pour une classe de systèmes différentiellement plats -- Adéquation de modèles pour une optimisation précise via la commande extrémale -- Application a une colonne pilote de flottation

PER4Mance Prototyping environment for research on human-machine interactions for alarm floods management: the case study of a chemical plant process control

ABSTRACT: Alarm floods are dangerous because the quantity of alarms triggered is too numerous for operators to reliably implement the right corrective action. Process operators of complex systems, such as chemical plants or nuclear power production, are faced with alarm management systems that can be better built in consideration of human capabilities and limitations. Developing human-machine interfaces (HMIs) that better support operators is critical for ensuring the safe and reliable operation of critical systems and processes. The research team has developed an accessible and adaptable prototyping environment dedicated for research on alarm management and human-machine interactions in the process industry. The method used was to build on the Tennessee Eastman Process (TEP) simulator and incorporate Human-Machine design guidelines. The results are an open-sourced prototyping environment that incorporates data from a real chemical plant and integrates true alarm data and thresholds. At the end of this article, we share the Github link to the entire MATLAB, Simulink and App Designer files of PER4Mance: a prototyping environment for research on human-machine interactions for alarm flood management