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Performance Assessment of Multivariate Control System Based on Data-Driven Covariance Historical Benchmark

  • Hong Qian
  • Gaofeng JiangEmail author
  • Yuan Yuan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 924)

Abstract

In order to better meet the on-site monitoring requirements and ensure the control system is running well, the performance assessment for multivariate control system (MIMO) is an increasing concern. According to the generalized eigenvalue analysis, multiple performance indexes based on the eigenvalues was proposed, and big data of process output was used to obtain a history data set and establish a historical benchmark, which can not only monitor the adjustment ability of single closed-loop, but also get the change of the overall control performance. A user-definable indicator is put forward to select the historical data set, then, based on the statistical analysis theory, the benchmark is improved and multiple performance levels are divided. Finally, the performance evaluation method is presented for MIMO. An example for monitoring the performance of the coordination control system (CCS) is illustrated to show the use and effectiveness and objectivity of the proposed method.

Keywords

Performance assessment Data-driven Multivariate control system Historical benchmark Statistical analysis theory Performance levels 

References

  1. 1.
    Desborough, L., Miller, R.: Increasing customer value of industrial control performance monitoring-honeywell’s experience.In: AIChE Symposium Series, pp. 169–189 (2002)Google Scholar
  2. 2.
    Harris, T.J.: Assessment of control loop performance. Can. J. Chem. Eng. 67(5), 856–861 (1989)CrossRefGoogle Scholar
  3. 3.
    Xin, Q., Yang, C., et al.: A review of control loop monitoring and diagnosis: prospects of controller maintenance in big data era. Chin. J. Chem. Eng. 24(8), 952–962 (2016)CrossRefGoogle Scholar
  4. 4.
    Zhang, G.M., Li, N., Li, S.Y.: A data driven performance monitoring method for predictive controller. J. Shanghai Jiao Tong Univ. 45(8), 1113–1118 (2011)Google Scholar
  5. 5.
    Zhang, Q., Li, S.Y.: Performance monitoring and diagnosis of multivariate model predictive control using statistical analysis. Chin. J. Chem. Eng. 14(2), 207–215 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Wang, X., Huang, B., Chen, T.: Multirate minimum variance control design and control performance assessment: a data-driven subspace approach. IEEE Trans. Control Syst. Technol. 15(1), 65–74 (2006)CrossRefGoogle Scholar
  7. 7.
    Bernstein, D.S., Haddad, W.M.: LQG control with an H∞ performance bound: a Riccati equation approach. In: American Control Conference, pp. 796–802. IEEE (2009)Google Scholar
  8. 8.
    Alghazzawi, A., Lennox, B.: Model predictive control monitoring using multivariate statistics. J. Process Control 19(2), 314–327 (2009)CrossRefGoogle Scholar
  9. 9.
    Yu, J., Qin, S.J.: Statistical MIMO controller performance monitoring, Part I: data-driven covariance benchmark. J. Process Control 18(3–4), 277–296 (2008)CrossRefGoogle Scholar
  10. 10.
    Yu, J., Qin, S.J.: Statistical MIMO controller performance monitoring, part II: Performance diagnosis. J. Process Control 18(3–4), 297–316 (2008)CrossRefGoogle Scholar
  11. 11.
    Li, Q., Whiteley, J.R., Rhinehart, R.R.: A relative performance monitor for process controllers. Int. J. Adapt. Control Sig. Process. 17(7–9), 685–708 (2010)zbMATHGoogle Scholar
  12. 12.
    Wang, L., Li, N.: Performance monitoring of the data-driven subspace predictive control systems based on historical objective function benchmark. Acta Autom. Sin. 39(5), 542–547 (2013)CrossRefGoogle Scholar
  13. 13.
    Desborough, L., Harris, T.: Performance assessment measures for univariate feedback control. Can. J. Chem. Eng. 70(6), 1186–1197 (1992)CrossRefGoogle Scholar
  14. 14.
    Mcnabb, C.A., Qin, S.J.: Projection based MIMO control performance monitoring: I—covariance monitoring in state space. J. Process Control 13(8), 739–757 (2003)CrossRefGoogle Scholar
  15. 15.
    Yuan, Q., Lennox, B., Mcewan, M.: Analysis of multivariate control performance assessment techniques. J. Process Control 19(5), 751–760 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Shanghai University of Electric PowerShanghaiChina
  2. 2.Shanghai Power Station Automation Technology Key LaboratoryShanghaiChina

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