Deep polytopic autoencoders for low-dimensional linear parameter-varying approximations and nonlinear feedback design

arXiv:2403.18044v1 Announce Type: cross
Abstract: Polytopic autoencoders provide low-dimensional parametrizations of states in a polytope. For nonlinear PDEs, this is readily applied to low-dimensional linear parameter-varying (LPV) approximations as they have been exploited for efficient nonlinear controller design via series expansions of the solution to the state-dependent Riccati equation. In this work, we develop a polytopic autoencoder for control applications and show how it outperforms standard linear approaches in view of LPV approximations of nonlinear systems and how the particular …

abstract arxiv autoencoders cs.lg cs.na design feedback linear low math.ds math.na math.oc physics.flu-dyn series solution state type via