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

Abstract 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.