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This paper presents State-space Model Interpolation of Local Estimates (SMILE), a technique to estimate linear parameter-varying (LPV) state-space models for multiple-input multiple-output (MIMO) systems whose dynamics depends on multiple time-varying parameters, called scheduling parameters. The SMILE technique is based on the interpolation of linear time-invariant models estimated for constant values...
This paper compares flat and hierarchical model structures in local model networks and discusses the side effects of normalization. A new algorithm for automatic transition adjustment between local models avoids undesirable effects that occur with the hierarchical approach and leads to a suitable model structure with better interpretability of local models. Demonstration examples illustrate the advantages...
Local model networks, also known as Takagi-Sugeno neuro-fuzzy systems, have become an increasingly popular nonlinear model architecture. Usually the local models are linearly parameterized and those parameters are typically estimated by some least squares approach. However, widely different strategies have been pursued for the partitioning of the input space which determines the validity regions of...
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