ABSTRACT

The robust linear parameters varying systems identification method with multiple-model approach is addressed in this paper. Various noise and outliers commonly exist in practical industrial processes and have a serious impact on data-driven system identification methods. A statistic approach is proposed in the paper where the centralised asymmetric Laplace (CAL) distribution is employed to model the noise and therefore the parameters estimation algorithm based on CAL distribution is robust to the symmetric/asymmetric noise and outliers. CAL distribution is represented as the product of exponential distribution and Gaussian distribution, and therefore the parameters estimation formulas are deduced in the expectation maximisation algorithm framework. The efficacy and the robustness of the proposed algorithm are verified by a numerical example and the continuous stirred tank reactor system experiment.

摘要

提出了一种基于多模型的鲁棒线性参数变化系统辨识方法。在实际的工业过程中通常存在各种噪声和异常值，这些噪声和异常值对数据驱动的系统识别方法有严重的影响。本文提出了一种统计方法，该方法采用集中式非对称拉普拉斯（CAL）分布对噪声进行建模，因此基于CAL分布的参数估计算法对对称/非对称噪声和离群值具有鲁棒性。CAL分布表示为指数分布和高斯分布的乘积，因此在期望最大化算法框架中推导了参数估计公式。