Print Email Facebook Twitter Time-Varying Human-Operator Identification with Box-Jenkins Models Title Time-Varying Human-Operator Identification with Box-Jenkins Models Author Ortiz Moya, Álvaro (TU Delft Aerospace Engineering; TU Delft Control & Simulation) Contributor Mulder, Max (mentor) van Paassen, M.M. (graduation committee) Pool, D.M. (graduation committee) de Winter, J.C.F. (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering | Control & Simulation Date 2023-12-06 Abstract The identification of time-varying, adaptive behaviour of a human operator in basic manual control tasks is undoubtedly under development since most methodologies only account for time-invariant systems. Previous authors have proved that estimation techniques based on ARX structures can generally identify the HO model parameters. Nonetheless, ARX methods present several problems, such as the persistent bias in estimates that may increase due to coupled noise and system models. Therefore, a novel identification technique based on Box Jenkins models is proposed to achieve a more adequate match between the estimator structure and the HO model. The identification process can be conducted offline by the Ordinary Least Squares and Prediction Error Method, or online, when Recursive Least Squares and Recursive PEM are employed, respectively, in ARX and BJ models. The BJ estimator has excellent potential as an identification tool due to its bias reduction capabilities, as clearly shown in batch-fitting, although non-linear optimization processes decrease its convergence speed by 500%. An RPEM algorithm with forgetting factor 𝝀 = 0.99609 and first-order remnant BJ structure is implemented and tested under Monte Carlo simulation and experimental data. Recursive BJ algorithms could help to achieve the ideal identification method by diminishing the Neuro-Muscular parameter bias in ARX. Subject ARXBox-JenkinsEqualizationHuman OperatorManual ControlPrediction Error MethodRecursive Prediction Error MinimizationRemnantTime-Varying Identification To reference this document use: http://resolver.tudelft.nl/uuid:1d979061-1c5a-466e-9935-46518d98e6e4 Embargo date 2024-01-01 Bibliographical note Invited graduation committee member professor Joost de Winter Part of collection Student theses Document type master thesis Rights © 2023 Álvaro Ortiz Moya Files PDF MSc_Thesis_AlvaroOrtizMoya_2023.pdf 77.68 MB Close viewer /islandora/object/uuid:1d979061-1c5a-466e-9935-46518d98e6e4/datastream/OBJ/view