Floquet-Parameterized Koopman-MPC for Tracking Periodic Orbits
Overview
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.
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- arXiv Robotics — research abstractsSignalFloquet-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.
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