• Corpus ID: 235265679

Control Occupation Kernel Regression for Nonlinear Control-Affine Systems

@article{Abudia2021ControlOK,
  title={Control Occupation Kernel Regression for Nonlinear Control-Affine Systems},
  author={Moad Abudia and Tejasvi Channagiri and Joel A. Rosenfeld and Rushikesh Kamalapurkar},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.00103}
}
This manuscript presents an algorithm for obtaining an approximation of nonlinear high order control affine dynamical systems, that leverages the controlled trajectories as the central unit of information. As the fundamental basis elements leveraged in approximation, higher order control occupation kernels represent iterated integration after multiplication by a given controller in a vector valued reproducing kernel Hilbert space. In a regularized regression setting, the unique optimizer for a… 

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