Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition

What is it about?

This manuscript presents a structure-exploiting nonlinear model reduction method for systems with general nonlinearities, which are typical in aerospace applications. The approach first uses nonlinear state space transformations to bring the model into a structured form. This structured model can then be efficiently reduced without the need for additional approximations (in contrast to state-of-the-art methods in model reduction). Our applications show that the approach is competitive in terms of reduced model accuracy with state-of-the-art model reduction, while having the added benefits of opening new pathways for rigorous analysis and input-independent model reduction via the introduction of the lifted problem structure.

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