Identification of Wiener-Hammerstein benchmark model via rank minimization
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- 作者：Younghee Han ; Raymond A. de Callafon
- 关键词：Nonlinear system identification ; Wiener&ndash ; Hammerstein systems ; Convex optimization ; Rank minimization ; SDP relaxation
- 刊名：Control Engineering Practice
- 出版年：2012
- 出版时间：November, 2012
- 年：2012
- 卷：20
- 期：11
- 页码：1149-1155
- 全文大小：628 K
文摘
In this paper, a Wiener-Hammerstein system identification problem is formulated as a semidefinite programming (SDP) problem which provides a sub-optimal solution for a rank minimization problem. In the proposed identification method, the first linear dynamic system, the static nonlinear function, and the second linear dynamic system are parameterized as an FIR model, a polynomial function, and a rational transfer function respectively. Subsequently the optimization problem is formulated by using the over-parameterization technique and an iterative approach is proposed to update two unmeasurable intermediate signals. For the modeling of static nonlinearity, the monotonically non-deceasing condition was applied to limit the number of possible selections for intermediate signals. At each step of iteration, the over-parametrized parameters are estimated and then system parameters are separated by using a singular value decomposition (SVD). The proposed method is applied to the benchmark problem and the estimation result shows the effectiveness of the proposed algorithm.