  In this paper, automated generation of linear parameter-varying (LPV)
state-space models to embed the dynamical behavior of nonlinear systems is
considered, focusing on the trade-off between scheduling complexity and model
accuracy and on the minimization of the conservativeness of the resulting
embedding. The LPV state-space model is synthesized with affine scheduling
dependency, while the scheduling variables themselves are nonlinear functions
of the state and input variables of the original system. The method allows to
generate complete or approximative embedding of the nonlinear system model and
also it can be used to minimize complexity of existing LPV embeddings. The
capabilities of the method are demonstrated on simulation examples and also in
an empirical case study where the first-principle motion model of a 3-DOF
control moment gyroscope is converted by the proposed method to LPV model with
low scheduling complexity. Using the resulting model, a gain-scheduled
controller is designed and applied on the gyroscope, demonstrating the
efficiency of the developed approach.
