A State-Space Approach to Identification of Wiener-Hammerstein Benchmark Model

Abstract We develop a state-space method of identifying a Wiener-Hammerstein system, where a nonlinearity is sandwiched by two linear systems. By dividing it into the linear system and Hammerstein system composed of the nonlinearity and the second linear system, we propose an iterative method of identifying the Hammerstein system by the orthogonal decomposition subspace method (ORT) and the linear system by minimizing the square norm of output prediction error, for which the identified Hammerstein model plays a role of instrument of measuring the output of the linear system. The data driven local coordinate (DDLC)-based gradient method is applied to this minimization. Numerical results for the benchmark problem are included to show the applicability of the present method.

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