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Kernel-Based Models for System Analysis
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DOI：10.1109/TAC.2022.3218944.png)
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Abstract
En 中文
This article introduces a computational framework to identify nonlinear input-output operators that fit a set of system trajectories while satisfying incremental integral quadratic constraints. The data fitting algorithm is thus regularized by suitable input-output properties required for system analysis and control design. This biased identification problem is shown to admit the tractable solution of a regularized least squares problem when formulated in a suitable reproducing kernel Hilbert space. The kernel-based framework is a departure from the prevailing state-space framework. It is motivated by fundamental limitations of nonlinear state-space models at combining the fitting requirements of data-based modeling with the input-output requirements of system analysis and physical modeling.
Keywords:
Identification for control
machine learning
modeling
nonlinear systems
system identification
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W