Simultaneous Parameter Estimation in Model-Free Control
Conference Year
2024
Abstract
Model-Free control is a data-driven control methodology that utilizes a surrogate model, namely the ultra-local mode, to provide a short-time estimate of the dynamics and uncertainties related to a nonlinear system. Traditional Model-Free control requires two parameters, one is estimated online while the other is considered a tuning parameter for the performance of the system at hand. The tuning parameter is typically chosen heuristically and depends upon expert knowledge of the system. This work provides a methodology for on-the-fly simultaneous selection of all parameters of the ultra-local model while using data from a robust Kalman Filter.
Primary Faculty Mentor Name
Luis Duffaut Espinosa
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Electrical Engineering
Primary Research Category
Engineering and Math Science
Simultaneous Parameter Estimation in Model-Free Control
Model-Free control is a data-driven control methodology that utilizes a surrogate model, namely the ultra-local mode, to provide a short-time estimate of the dynamics and uncertainties related to a nonlinear system. Traditional Model-Free control requires two parameters, one is estimated online while the other is considered a tuning parameter for the performance of the system at hand. The tuning parameter is typically chosen heuristically and depends upon expert knowledge of the system. This work provides a methodology for on-the-fly simultaneous selection of all parameters of the ultra-local model while using data from a robust Kalman Filter.