Floquet-Parameterized Koopman-MPC for Tracking Periodic Orbits
Floquet-Parameterized Koopman-MPC for Tracking Periodic Orbits
This paper introduces a physics-informed control method that integrates Floquet theory with Koopman operator learning to achieve real-time tracking of periodic orbits in nonlinear systems. By leveraging monodromy matrices and extended dynamic mode decomposition (EDMD), the approach learns linear predictors in lifted space constrained by periodic stability information. The resulting model predictive control (MPC) law is evaluated on a cartpole system under periodic forcing, with performance compared to purely data-driven baselines.
The authors propose a physics-informed control framework combining Floquet theory and Koopman operator learning to enable real-time tracking of periodic orbits in nonlinear systems, evaluated on a cartpole benchmark against data-driven baselines.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org