Performance Assessment of Control Loop with Time-variant Disturbance Dynamics Based on Multi-model Mixing Minimum Variance Control
-
摘要: 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.Abstract: 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.
-
Key words:
- Multiple models /
- mixing /
- minimum variance /
- time-variant disturbance /
- performance assessment
-
[1] Astrom K J. Introduction to Stochastic Control Theory. New York: Academic, 1970. [2] Harris T. Assessment of control loop performance. Canadian Journal of Chemical Engineering, 1989, 67(5): 856-861 [3] Stanfelj N, Marlin T E, MacGregor J F. Monitoring and diagnosing process control performance: the single-loop case. Industrial and Engineering Chemistry Research, 1993, 32(2): 301-314 [4] Tyler M, Morari M. Performance assessment for unstable and nonminimum-phase systems. In: Proceedings of the 1996 IFAC Workshop on On-Line Fault Detection and Supervision in the Chemical Process Industries. Tyne, UK: IFAC, 1996. 187-192 [5] Olaleye F, Huang B, Tamayo E. Performance assessment of control loops with time-variant disturbance dynamics. Journal of Process Control, 2004, 14(8): 867-877 [6] Zhou M F, Xie L, Pan H T,Wang S Q. Performance assessment of PID controller with time-variant disturbance dynamics. In: Proceedings of the 2011 International Symposium on Advanced Control of Industrial Processes. Hangzhou, China: IEEE, 2011. 650-655 [7] Huang B. Minimum variance control and performance assessment of time variant processes. Journal of Process Control, 2002, 12(6): 707-719 [8] Xu F W, Huang B. Performance monitoring of SISO control loops subject to LTV disturbance dynamics: an improved LTI benchmark. Journal of Process Control, 2006, 16(6): 567-579 [9] Huang B, Shah S L. Performance Assessment of Control Loops: Theory and application. New York: Springer, 1999 [10] Roderick M S, Johansen T A. Multiple Model Approaches to Modeling and Control. London: Taylor and Francis, 1997. [11] Dong Zhi-Kun, Wang Xin, Wang Xiao-Bo, Li Shao-Yuan, Zheng Yi-Hui. Application of weighted multiple models adaptive controller in the plate cooling process. Acta Automatica Sinica, 2010, 36(8): 1144-1150(董志坤, 王昕, 王笑波, 李少远, 郑益慧. 多模型加权自适应控制在中厚板层流冷却系统中的应用. 自动化学报, 2010, 36(8): 1144-1150) [12] Zheng Yi-Hui, Wang Xin, Li Shao-Yuan. Multiple models direct adaptive decoupling controller for stochastic systems. Acta Automatica Sinica, 2010, 36(9): 1295-1304(郑益慧, 王昕, 李少远. 随机系统的多模型自适应解耦控制器. 自动化学报, 2010, 36(9): 1295-1304) [13] Wang Xin, Li Shao-Yuan, Yue Heng. Multivariable adaptive decoupling controller using hierarchical multiple models. Acta Automatica Sinica, 2005, 31(2): 223-230(王昕, 李少远, 岳恒. 分层递阶多模型自适应解耦控制器. 自动化学报, 2005, 31(2): 223-230) [14] Matthew K, Petros I. Multiple model adaptive control with mixing. IEEE Transactions on Automatic Control, 2010, 55(8): 1822-1836 [15] Simone B, Petros I, Edoardo M. Multiple model adaptive mixing control: the discrete-time case. IEEE Transactions on Automatic Control, 2012, 57(4): 1040-1045 [16] Chen Ming-Jie, Lan Hai, Sun Shi-Feng. GA in fuzzy PID control of the boiler steam pressure. Techniques of Automation and Applications, 2008, 27(1): 12-16(陈明杰, 兰海, 孙世峰. 遗传算法在锅炉蒸汽压力模糊PID控制中的应用研究. 自动化技术与应用, 2008, 27(1): 12-16)
点击查看大图
计量
- 文章访问数: 1845
- HTML全文浏览量: 80
- PDF下载量: 830
- 被引次数: 0