For an industrial control system, controller parameters are important factors that affect the control system performance. This article introduces a controller parameter performance assessment method based on disturbance characteristic variation for a dynamic matrix control system. Assuming that the process model is accurate and the setpoint is constant, disturbance characteristic variation without adjusting controller parameters leads to the degradation of system performance. Hence, the Markov parameters of a disturbance model, which inflect the variation of disturbance characteristics, are used to assess the controller parameter performance. These Markov parameters can be obtained from closed‐loop data using the subspace identification method. The differences between the Markov parameters in an actual running state and those in a well‐regulated state are used to design the assessing index of controller parameters. The simulation results in the Wood–Berry model and TE process show the validity of the proposed index.

中文翻译：

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通过估计扰动的马尔可夫参数评估动态矩阵控制控制器参数

对于工业控制系统，控制器参数是影响控制系统性能的重要因素。本文介绍了一种基于扰动特性变化的动态矩阵控制系统控制器参数性能评估方法。假设过程模型是准确的并且设定点是恒定的，则在不调整控制器参数的情况下干扰特性的变化会导致系统性能下降。因此，扰动模型的马尔可夫参数（会影响扰动特性的变化）用于评估控制器参数的性能。这些马尔可夫参数可以使用子空间识别方法从闭环数据中获得。实际运行状态下的马尔可夫参数与良好调节状态下的马尔可夫参数之间的差异用于设计控制器参数的评估指标。Wood-Berry模型和TE过程的仿真结果证明了所提出指标的有效性。