In the development of Model Predictive Control (MPC) systems, having a data-driven approach to automatically identify black-box dynamical models that are both accurate and computationally efficient is of paramount importance. In this work, we propose an extension of the learning algorithm proposed in [1] to reduce the state/model size during the training procedure by employing RESNETs and grouplasso, thus easing its usage in embedded MPC applications. Performance will be assessed on the Silverbox benchmark.
Nonlinear systems identification with automatic state/model size reduction using RESNETs and Group Lasso
L. Frascati;A. Bemporad
2023-01-01
Abstract
In the development of Model Predictive Control (MPC) systems, having a data-driven approach to automatically identify black-box dynamical models that are both accurate and computationally efficient is of paramount importance. In this work, we propose an extension of the learning algorithm proposed in [1] to reduce the state/model size during the training procedure by employing RESNETs and grouplasso, thus easing its usage in embedded MPC applications. Performance will be assessed on the Silverbox benchmark.File in questo prodotto:
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