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In this paper, automated generation of linear parameter-varying (LPV)state-space models to embed the dynamical behavior of nonlinear systems isconsidered, focusing on the trade-off between scheduling complexity and modelaccuracy and on the minimization of the conservativeness of the resultingembedding. The LPV state-space model is synthesized with affine schedulingdependency, while the scheduling variables themselves are nonlinear functionsof the state and input variables of the original system. The method allows togenerate complete or approximative embedding of the nonlinear system model andalso it can be used to minimize complexity of existing LPV embeddings. Thecapabilities of the method are demonstrated on simulation examples and also inan empirical case study where the first-principle motion model of a 3-DOFcontrol moment gyroscope is converted by the proposed method to LPV model withlow scheduling complexity. Using the resulting model, a gain-scheduledcontroller is designed and applied on the gyroscope, demonstrating theefficiency of the developed approach.

Affine Linear Parameter-Varying Embedding of Nonlinear Models with Improved Accuracy and Minimal Overbounding