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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。
Aiming at a kind of double-rate sampling CARMA model, the related self-tuning control problem is studied. Based on double-rate sampling and noise-containing data, an auxiliary model is proposed to estimate the lossless output data which can not be sampled, and the stochastic gradient algorithm is further used to estimate the model parameters. By minimizing the variance of the optimal prediction output and combining with the Diophantine equation, a generalized minimum variance self-tuning control (AM-GMVSTC) strategy based on the auxiliary model is given. Finally, a simulation example shows the effectiveness of the proposed algorithm.