An optimal control algorithm based on Kalman filter for ARMA disturbances

机译：基于卡尔曼滤波器的ARMA扰动最优控制算法

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Harmonic rule is popularly used in machine setup adjustment problems introduced by Grubbs (1954). The algorithm is optimal when the disturbance process is white noise and the initial process bias is an unknown value. When the initial process bias is assumed to be a random variable with a priori distribution, Grubbs' extended rule is optimal when the disturbance process is white noise. This paper considers the case that the initial process bias is a random variable and the disturbance process is a general ARMA(p, q) process. Under the framework of state-space model and based on Bayesian rule, an optimal control algorithm is derived. Several illustrative numerical examples are given through Monte Carlo simulations.

机译：谐波规则普遍用于由Grubbs（1954）提出的机器设置调整问题中。当干扰过程是白噪声并且初始过程偏差是未知值时，该算法是最佳的。当假定初始过程偏差是具有先验分布的随机变量时，当干扰过程是白噪声时，Grubbs的扩展规则是最佳的。本文考虑初始过程偏差是随机变量而干扰过程是一般的ARMA（p，q）过程的情况。在状态空间模型的框架下，基于贝叶斯规则，推导了一种最优控制算法。通过蒙特卡洛模拟给出了几个说明性的数值示例。

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《2011 IEEE International Conference on Intelligence and Security Informatics》|2011年|p.345-348|共4页
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He Fangyi;
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中图分类人工智能理论;
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An optimal control algorithm based on Kalman filter for ARMA disturbances

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