Print Email Facebook Twitter Cybernetic Data Augmentation for Neural Network Classification of Control Skills Title Cybernetic Data Augmentation for Neural Network Classification of Control Skills Author de Jong, M.A. (TU Delft Electrical Engineering, Mathematics and Computer Science) Pool, D.M. (TU Delft Control & Simulation) Mulder, Max (TU Delft Control & Simulation) Faculty Electrical Engineering, Mathematics and Computer Science Date 2022 Abstract Mathematical human controller (HC) models are widely used in tuning manual control systems and for understanding human performance. Typically, quasi-linear HC models are used, which can accurately capture the linear portion of HCs' behavior, averaged over a long measurement window. This paper presents a deep learning HC skill-level evaluation method that works on short windows of raw HC time signals, and accounts for both the linear and non-linear portions of HC behavior. This deep learning approach is applied to data from a previous skill training experiment performed in the SIMONA Research Simulator at TU Delft. Additional human control data is generated using cybernetic HC model simulations. The results indicate that the deep learning evaluation method is successful in predicting HC skill level with 85-90% validation accuracy, but that training the classifier solely on simulated HC data reduces this accuracy by 15-25%. Inspection of the results especially shows a strong sensitivity of the classifier to the presence of remnant in the simulated training data. In conclusion, these results reveal that current quasi-linear HC model simulations, and in particular the remnant portion, do not adequately capture real time-domain HC behavior to allow effective training-data augmentation. Subject classificationcontrol skillsCyberneticsmanual controlneural networks To reference this document use: http://resolver.tudelft.nl/uuid:6cca46e6-d492-4b41-b5ef-4649714499c9 DOI https://doi.org/10.1016/j.ifacol.2022.10.252 ISSN 1474-6670 Source IFAC-PapersOnLine, 55 (29), 178-183 Event 15th IFAC Symposium on Analysis, Design and Evaluation of Human Machine Systems, HMS 2022, 2022-09-12 → 2022-09-15, San Jose, United States Part of collection Institutional Repository Document type journal article Rights © 2022 M.A. de Jong, D.M. Pool, Max Mulder Files PDF 1_s2.0_S2405896322022790_main.pdf 1.46 MB Close viewer /islandora/object/uuid:6cca46e6-d492-4b41-b5ef-4649714499c9/datastream/OBJ/view