基于多模型混合最小方差控制的时变扰动控制系统性能评估

在实际工业过程中，控制系统经常会受到时变扰动的影响，致使针对单一扰动模型设计的最小方差控制准则不再适用于评估时变扰动控制系统的性能. 当多个扰动信号同时出现时，采用常规多模型切换方法会发生间歇切换进而产生较大的暂态误差，不能准确评估系统当前性能. 针对上述问题，本文提出了一种基于多模型混合最小方差控制准则的性能评估方法. 首先根据每个扰动模型分别制定最小方差控制器，组成多模型最小方差控制器，然后在每个时间点混合多模型最小方差控制器，并将在其作用下的输出方差作为最终的性能评估基准，该方法既 充分考虑到每个扰动的特性，又避免了常规多模型切换方法因间歇切换而产生的暂态误差对评估结果准确性带来的影响，实现了准确、可靠地评估时变扰动控制系统的性能. 通过仿真，验证了基于多模型混合最小方差控制准则的性能评估方法的有效性.

Performance Assessment of Control Loop with Time-variant Disturbance Dynamics Based on Multi-model Mixing Minimum Variance Control

In an actual industrial process, the control system is often affected by the time-variant disturbances, so the minimum variance control design based on a single disturbance model as the benchmark is no longer useful to assess the time-variant disturbance control loop performance. If several disturbances appear at the same time, a conventional method of multi-model switching could result in intermittent switching and a larger transient error, failing to evaluate the current system performance accurately. Therefore this paper proposes a multi-model mixing minimum variance control for performance assessment. Firstly, one minimum variance controller is designed for each disturbance characteristic, such that a multiple model minimum variance controller can be formed. Then at each time point, multiple minimum variance controllers are mixed, and the output variance is the final performance evaluation benchmark. This method fully considers the feature of each disturbance and avoids the intermittent switching and larger transient error caused by the conventional multi-model switching method, realize accurate of and reliable assess of the time-variant disturbance performance. Finally, the effectiveness of the multi-model mixing minimum variance control as the benchmark to assess time-variant disturbance control systems is verified through simulations.