Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application. (8th June 2022) - Liang, Shujun; Zhang, Huanlong; Zhang, Jie; Wang, Fengxian
Abstract : In the process of actual system modeling, many systems exhibit nonlinear characteristics with memory. Thus, the parameter identification problem of the nonlinear system with memory usually appears in the system modeling. This report focuses on the nonlinear system identification of Wiener–Hammerstein-like model with memory hysteresis, in which a new recursive estimation way is introduced. In this algorithm, the estimation bias problem can be improved by introducing a data filtering technique. On the basis of the filtered data, some auxiliary matrices and vectors are proposed. Following this, the identification error variable is introduced by using auxiliary matrices and vectors with an adaptive forgetting factor. Afterward, the identification error variable is integrated into the design of parameter estimation adaptive law with recursive gain structure. By comparison with the classic estimation methods, the proposed algorithm shows an alternative identification algorithm design angle. In addition, it is strictly proved that the parameter estimation error converges to zero under a general excitation condition. Based on the results of indices M S E, compared with the existing methods, the performance improvements of the proposed method are 33.9 %, 41.26%, and 53.5%, respectively. In terms of indices P E M, the augmented performances of the developed scheme are 50%, 56.2%, and 68.4%, respectively, in comparison to the available schemes.