| Summary: | In this study, a new modelling and control framework based on type 2 fuzzy logic and validated with real-time experiments on human participants experiencing stress via mental arithmetic cognitive tasks is presented. The ultimate aim of the proposed modelling and control framework is the management and ultimately the prevention of performance breakdown in a human-computer interaction system with a special focus on human performance. This work starts with a literature-based study of previously successful experimental designs, selecting the mental arithmetic operations cognitive task for its ease of implementation and validated through a series of statistical tests on 12 participants as far as its influence on commonly used psychophysiological markers is concerned. Additionally, a new marker for mental stress identification is introduced, the pupil diameter marker; validated with the same series of statistical tests for all 12 participants in the study. For the validation of the introduced modelling and control techniques, two designed experiments which consist of carrying-out arithmetic operations of varying difficulty levels were performed by 10 participants (operators) in the study. With this new technique, effective modelling is achieved through a new adaptive, self-organising and interpretable modelling framework based on General Type-2 Fuzzy sets. This framework is able to learn in real-time through the implementation of a re-structured performance-learning algorithm that identifies important features in the data without the need for prior training. The information learnt by the model is later exploited via an Energy Model Based Controller that infers adequate control actions by changing the difficulty levels of the arithmetic operations in the human-computer-interaction system; these actions being based on the most current psychophysiological state of the subject under study. The successful real-time implementation of the proposed adaptive modelling and control strategies within the framework of the human-machine-interaction under study shows superior performance as compared to other forms of modelling and control, with minimal intervention in terms of model re-training or parameter re-tuning to deal with uncertainties, disturbances and inter/intra-subject parameter variability.
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