Robust Global Identification of LPV Errors-in-Variables Systems With Incomplete Observations
针对观测数据随机缺失和存在异常值的情况，提出一种鲁棒全局策略来辨识线性参数变化（LPV）变量含误差（EIV）系统，利用期望最大化算法和Student t分布处理非理想观测，并通过粒子滤波近似隐状态后验分布。
This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input is investigated and a nonlinear state-space model is considered for the input generation model (IGM). The parameters estimation of the LPV EIV systems with nonideal observations is realized using the expectation–maximization algorithm which is particular effective for the incomplete data issue. To ensure the robustness in the identification, the Student’s t-distribution which is characterized by its adjustable degree of freedom, is used to handle the measurement non-normality. Since the posterior distributions of the latent states in the IGM are also involved in the identification process and they are difficult to calculate directly, the particle filter is introduced to recursively approximate them instead. Finally, the verification examples are given to demonstrate the effectiveness of the developed strategy.