Adaptive filtering-based recursive identification for time-varying Wiener output-error systems with unknown noise statistics. (English) Zbl 1429.93400
Summary: 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.
MSC:
| 93E12 | Identification in stochastic control theory |
| 93E11 | Filtering in stochastic control theory |
| 93C10 | Nonlinear systems in control theory |
| 93E10 | Estimation and detection in stochastic control theory |
| 93B35 | Sensitivity (robustness) |
Keywords:
adaptive filtering recursive identification; Wiener output-error systems; martingale hyperconvergence theoremReferences:
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