双率CARMA模型的广义最小方差自校正控制

Generalized minimum variance self-tuning control for a dual-rate CARMA model

针对一类双率采样的CARMA模型,研究了相关的自校正控制问题。基于双率采样以及含有噪声的数据,本文提出一个辅助模型来估计无法采样到的损失输出数据,并进一步采用随机梯度算法来估计模型参数。通过最小化最优预测输出的方差并结合Diophantine方程给出了基于辅助模型的广义最小方差自校正控制(AM-GMVSTC)策略。最后通过一个仿真例子说明提出算法的有效性。

This paper considers the problem of self-tuning control for a class of dual-rate sampled CARMA model. Based on the dual-rate sampled and noise-contaminated data, an auxiliary model is presented to estimate the missing output data, and further a stochastic gradient algorithm is introduced to estimate the parameters of model. By using a Diophantine equation and minimizing the variance of the optimal prediction errors, the auxiliary model based generalized minimum self-tuning control （AM-GMVSTC） strategy can be derived. A numerical simulation example illustrates the effectiveness of the presented algorithm.