Identification of LPV Models with Non-uniformly Spaced Operating Points by Using Asymmetric Gaussian Weights

In this paper, asymmetric Gaussian weighting functions are introduced for the identification of linear parameter varying systems by utilizing an input–output multi-model structure. It is not required to select operating points with uniform spacing and more flexibility is achieved. To verify the effectiveness of the proposed approach, several weighting functions, including linear, Gaussian and asymmetric Gaussian weighting functions, are evaluated and compared. It is demonstrated through simulations with a continuous stirred tank reactor model that the proposed approach provides more satisfactory approximation.

In the field of nonlinear system identification, the linear parameter varying (LPV) model identification approach has attracted more and more attention from academia and industry. In this paper, the asymmetric Gaussian weighting function is introduced into nonlinear identification of the multi-model LPV structure. Choosing uneven operating points for local linear models over the scheduling variable, the accuracy and flexibility of the multi-model LPV model can be improved. Simulation results of a CSTR benchmark process demonstrate that the new LPV model using asymmetric Gaussian weights is more accurate and more flexible than the existing LPV models commonly using Gaussian weights or linear weights.Figure optionsDownload as PowerPoint slide