Kernel Methods for Linear Discrete-Time Equations
arXiv:1507.03111v2
AI Analysis
This work addresses system identification and control stabilization for engineers and researchers, but appears incremental as it applies existing kernel methods to a known domain.
The paper tackles the problem of estimating system matrices for linear dynamical and control systems using learning theory methods, with results demonstrated through numerical examples and an application to stabilization via algebraic Riccati equations.
Methods from learning theory are used in the state space of linear dynamical and control systems in order to estimate the system matrices. An application to stabilization via algebraic Riccati equations is included. The approach is illustrated via a series of numerical examples.