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 and Scheduling Dimension Reduction (SDR) for Linear Parameter-Varying (LPV) State-Space (SS) models%2C comparing and benchmarking their capabilities%2C limitations and performance. The use case chosen for these studies is an interconnected network of nonlinear coupled mass spring damper systems with three different configurations%2C where some spring coefficients are described by arbitrary user-defined static nonlinear functions. For SOR%2C the following methods are compared: Linear Time-Invariant (LTI)%2C LPV and LFR-based balanced reductions%2C moment matching and parameter-varying oblique projection. For SDR%2C the following methods are compared: Principal Component Analysis (PCA)%2C trajectory PCA%2C Kernel PCA and LTI balanced truncation%2C autoencoders and deep neural network. The comparison reveals the most suitable reduction methods for the different benchmark configurations%2C from which we provide use case SOR and SDR guidelines that can be used to choose the best reduction method for a given LPV-SS model.?quality=80&w=800)
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