Systems identification of Hammerstein nonlinear systems for dual-rate sampling and output signal quantized

To the Hammerstein nonlinear systems with dual-rate sampling and output signal quantization, an auxiliary model based system identification method for dual-rate sampling Hammerstein quantized systems is presented by employing repeated stochastic empirical output measurements. The model features of dual-rate Hammerstein sampling system and a two-step identification strategy are first presented under relaxed estimated error condition. The persistent exciting condition for parameter identification is derived. The auxiliary model based parameter recursive identification algorithm for dual-rate sample Hammerstein nonlinear quantized systems is also given then. Convergence analysis of the auxiliary model based quantized identification recursive algorithm provides an upper bound value for parameter identification error estimation. Finally, simulation results show the effectiveness of the conclusions.