采用最优状态估计的主蒸汽温度动态矩阵控制方法
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(西安交通大学电子与信息工程学院,西安710049)

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E-mail: zjb15209275407@stu.xjtu.edu.cn.

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TP273

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国家自然科学基金项目(61673315);西安交通大学基本科研业务费研究项目(xjj2014048).


Dynamic matrix control for main steam temperature control system based on optimal state estimation
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(School of Electronic and Information Engineering,Xián Jiaotong University,Xián 710049,China)

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    摘要:

    针对主蒸汽温度控制系统不可测扰动影响的问题,提出一种基于最优状态估计的动态矩阵控制(OSE-DMC)算法.首先分析主蒸汽温度系统的动态特性,归纳出减温水量对主蒸汽温度影响的数学模型;然后基于常规DMC算法,利用最优估计理论对DMC算法的预测序列进行补偿修正,有效抑制不可测扰动的影响,并提出一种Kalman滤波的降维算法,使其计算更加简洁、便于实际应用与在线计算.Simulink仿真结果表明:在不可测扰动影响下,所提出方法抑制扰动效果比常规DMC算法提高20%,抑制扰动能力有显著提升.

    Abstract:

    A method of dynamic matrix control based on optimal state estimation(OSE-DMC) is proposed for unmeasurable disturbances in a main steam temperature control system. Firstly, the dynamic characteristics and the mathematical model of main steam temperature with the disturbance of spray attemperator water are concluded based on the structure of the main steam temperature system. Then, a Kalman filter is designed to compensate the predictive sequence in the DMC algorithm according to the optimal state estimation theory. The influence of unmeasurable disturbance is effectively suppressed. In addition, a reduced-order algorithm for the Kalman filter is proposed to simplify calculation, which is convenient for practical application and on-line calculation. Simulation results show that the proposed method can increase the effect of disturbance suppression by 20% compared with the conventional DMC algorithm, and the capability of disturbance suppression is significantly improved.

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胡怀中,张建博,杨清宇.采用最优状态估计的主蒸汽温度动态矩阵控制方法[J].控制与决策,2019,34(7):1475-1480

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  • 在线发布日期: 2019-06-28
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