Two-stage auxiliary model gradient-based iterative algorithm for the input nonlinear controlled autoregressive system with variable-gain nonlinearity

This article focuses on the parameter estimation problem of the input nonlinear system where an input variable-gain nonlinear block is followed by a linear controlled autoregressive subsystem. The variable-gain nonlinearity is described analytical by using an appropriate switching function. According to the gradient search technique and the auxiliary model identification idea, an auxiliary model-based stochastic gradient algorithm with a forgetting factor is presented. For the sake of improving the parameter estimation accuracy, an auxiliary model gradient-based iterative algorithm is proposed by utilizing the iterative identification theory. To further optimize the performance of the algorithm, we decompose the identification model of the system into two submodels and derive a two-stage auxiliary model gradient-based iterative (2S-AM-GI) algorithm by using the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms and show that the 2S-AM-GI algorithm has higher identification efficiency compared with the other two algorithms.