Auxiliary Model-Based Iterative Estimation Algorithms for Nonlinear Systems Using the Covariance Matrix Adaptation Strategy

This paper focuses on the parameter estimation problem of Hammerstein nonlinear systems with colored noise. By virtue of the covariance matrix adaptation strategy and the auxiliary model identification idea, an auxiliary model-based covariance matrix adaptation (AM-CMA) identification algorithm is presented. Furthermore, for the purpose of improving the optimization efficiency of the AM-CMA algorithm, we introduce the gradient search into the AM-CMA algorithm and propose an auxiliary model-based covariance matrix and gradient search adaptation (AM-CMGA) identification algorithm. The proposed algorithms are efficient and can give satisfactory parameter estimation results. The proposed algorithms are efficient and can give satisfactory parameter estimation results. The simulation examples are provided to demonstrate the effectiveness of our approaches.