A State of the Art in Feedforward-Feedback Learning Control Systems for Human Errors Prediction

Kiswendsida Abel Ouedraogo (1) , Simon Enjalbert (1) , Frédéric Vanderhaegen (1)

1 LAMIH - Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201

In this paper, authors propose an overview of feedforward-feedback learning control systems that can be adapted for human errors prediction. A State of the Art in existing approaches for machines of feedback and/or feedforward learning control systems is presented and a synthesis relevant for prediction purposes is detailed. The possible application for learning systems based on human errors applied to Human Machine System (HMS) is then identified. A feedforward-feedback learning system applied to car driving simulation in order to predict intentional human errors is proposed. The paper concludes on relevant perspectives for feedforward-feedback learning systems to predict human errors and to increase HMS resilience facing unplanned disruptions in transportation.

Mots clés: Human Machine Systems; human error prediction; learning systems

Domaines: Sciences de l'ingénieur [physics]; Automatique / Robotique

Kiswendsida Abel Ouedraogo, Simon Enjalbert, Frédéric Vanderhaegen. A State of the Art in Feedforward-Feedback Learning Control Systems for Human Errors Prediction. 18th World Congress of the International Federation of Automatic Control (IFAC), Aug 2011, Milano, Italy. pp.7390-7395, ⟨10.3182/20110828-6-IT-1002.03483⟩. ⟨hal-03644268⟩