Compressive sensing-based Preisach hysteresis model identification

The Preisach hysteresis model has been adopted extensively in the modeling of magnetic and smart material-based systems. Fidelity of the model hinges on accurate identification of the Preisach density function. Existing work on the identification of density function usually involves applying an input that contains sufficient excitation and measuring a large set of output data. In this paper, we propose a novel compressive sensing-based Preisach model identification approach that requires much fewer measurements. The density function is transformed into the frequency domain, generating a sparse signal of discrete cosine transform (DCT) coefficients, which can be efficiently reconstructed using compressive sensing algorithms. The root-mean-square error (RMSE) and the maximum absolute error are adopted to examine the density function reconstruction capability and the model estimation performance. The effectiveness of the proposed identification scheme is illustrated through both simulation results and experiments involving a vanadium dioxide (VO₂)-integrated microactuator.