Multi-rate systems exist widely in the field of computer process control. Research on the multi-rate issue of linear systems is relatively mature and is gradually turning to multi-rate issues of nonlinear systems. Concurrently, irregular process disturbances (impulse noise, outliers, non-zero mean noise) and varying transmission delay problems are often overlooked or considered alone, which is lacking generality. Thus, the main objective of this paper is to formulate and solve the identification problem of multi-rate nonlinear Wiener models with time-varying delay and irregular process disturbances simultaneously. The probability graph model of the Wiener process is constructed. Under the expectation–maximization (EM) framework, the Scale outlier model and Location outlier model (both based on Gaussian mixture distribution) are separately introduced to model the contaminated output data. The time-varying delay at each sampling instant is assumed as a uniform distribution. An auxiliary model is applied to acquire the unmeasured middle variable. Then, a robust EM identification algorithm is developed and its convergence is analyzed. The validity of the developed approach is illustrated through two numerical examples and a simulation of the continuous stirred tank reactor.

多速率系统广泛存在于计算机过程控制领域。线性系统多速率问题的研究比较成熟，正在逐渐转向非线性系统的多速率问题。同时，不规则的过程干扰（脉冲噪声、异常值、非零均值噪声）和变化的传输延迟问题往往被忽视或单独考虑，缺乏普遍性。因此，本文的主要目标是制定和解决同时具有时变延迟和不规则过程扰动的多速率非线性维纳模型的识别问题。构建了维纳过程的概率图模型。在期望最大化（EM）框架下，分别引入Scale离群模型和Location离群模型（均基于高斯混合分布）对污染输出数据进行建模。假设每个采样时刻的时变延迟是均匀分布的。应用辅助模型来获取未测量的中间变量。然后，开发了一种鲁棒的电磁识别算法并分析了它的收敛性。通过两个数值例子和连续搅拌釜反应器的模拟说明了所开发方法的有效性。