Koopman NMPC: Koopman-based Learning and Nonlinear Model Predictive Control of Control-affine Systems

@article{Folkestad2021KoopmanNK,
  title={Koopman NMPC: Koopman-based Learning and Nonlinear Model Predictive Control of Control-affine Systems},
  author={Carl Folkestad and Joel W. Burdick},
  journal={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2021},
  pages={7350-7356}
}
  • C. Folkestad, J. Burdick
  • Published 17 May 2021
  • Engineering
  • 2021 IEEE International Conference on Robotics and Automation (ICRA)
Koopman-based learning methods can potentially be practical and powerful tools for dynamical robotic systems. However, common methods to construct Koopman representations seek to learn lifted linear models that cannot capture nonlinear actuation effects inherent in many robotic systems. This paper presents a learning and control methodology that is a first step towards overcoming this limitation. Using the Koopman canonical transform, control-affine dynamics can be expressed by a lifted… 

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