Adaptive filtering-based recursive identification for time-varying Wiener output-error systems with unknown noise statistics

In area of control, model-based robust identification is rare, and studies in presence of unknown noise statistics are especially seldom. The robust estimation problem for time-varying Wiener output-error systems is considered in this paper. An adaptive filtering-based recursive identification scheme is proposed to distinguish nonlinear time-varying characteristics in complex noise environments. Firstly, a virtual equivalent state space model is constructed to achieve adaptive Kalman filtering. In filter design, a weighted noise estimator based on Sage-Husa principle is introduced, and is sensitive to noise changes. Secondly, the state estimates obtained by filters are used to form the unknown intermediate variables in information vectors. Then, a recursive estimation method based on multiple iterations is developed, and the convergence of identification is confirmed by martingale hyperconvergence theorem. Finally, the numerical simulation results verify the theoretical findings. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.