SOURCE-LINKED INTELLIGENCE
Port-Hamiltonian Koopman Operator Synthesis for Mechanical Systems
Finite-dimensional Koopman models enable efficient linear prediction and control of nonlinear robotic systems. However, models learned purely from trajectory data may violate the energetic structure of the underlying mechanics, producing predictions that exhibit artificial energy growth and diverge under recursive propagation. This work presents a structure-preserving Koopman framework for Euler-Lagrange systems built on generalized-momentum coordinates. The momentum transformation exposes the mechanical actuation as a known, state-independent port, which is preserved explicitly in the lifted
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T14:31:18.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.