RTD-A Control for General Multivariable Systems

Research Article - Chemical Engineering
  • 13 Downloads

Abstract

For general multivariable systems including square ones and fat and thin non-square ones, a unified and analytical optimum control law has been derived for the RTD-A controller. A concise interpretation for the general existence of the control law is provided together with a suggestion for judging this existence in practical applications. Three simulation examples are included to demonstrate the flexibility, friendliness, and excellent robustness, anti-noise performance and dynamic control capability of the RTD-A controller.

Keywords

Multivariable system Non-square system RTD-A Robustness Anti-noise performance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This research work was funded by National Natural Science Foundation of China (Project Number: 21676012) and Fundamental Research Funds for the Central Universities (Project Number: YS1404)

References

  1. 1.
    Forbes, M.G.; Patwardhan, R.S.; Hamadah, H.; Gopaluni, R.B.: Model predictive control in industry: challenges and opportunities. IFAC-PapersOnLine 48(8), 531–538 (2015)CrossRefGoogle Scholar
  2. 2.
    Cutler, C.R.; Ramaker, B.L.: Dynamic Matrix Control—A Computer Control Algorithm. In: Proceedings of the Joint Automatic Control Conference, San Francisco (1980)Google Scholar
  3. 3.
    Xi, Y.G.; Li, D.W.; Lin, S.: Model predictive control—status and challenges. Acta Autom. Sin. 39(3), 222–236 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Scattolini, R.: Architectures for distributed and hierarchical model predictive control—a review. J. Process Control 19(5), 723–731 (2009)CrossRefGoogle Scholar
  5. 5.
    Rivera, D.E.; Morari, M.; Skogestad, S.: Internal model control: PID controller design. Ind. Eng. Chem. Process Des. Dev. 25(1), 2163–2163 (1986)CrossRefGoogle Scholar
  6. 6.
    Anwar, M.N.; Shamsuzzoha, M.; Pan, S.: A frequency domain PID controller design method using direct synthesis approach. Arab. J. Sci. Eng. 40(4), 995–1004 (2015)CrossRefGoogle Scholar
  7. 7.
    Ogunnaike, B.A.; Mukati, K.: An alternative structure for next generation regulatory controllers: part I: basic theory for design, development and implementation. J. Process Control l6(5), 499–509 (2006)CrossRefGoogle Scholar
  8. 8.
    Mukati, K.; Rasch, M.; Ogunnaike, B.A.: An alternative structure for next generation regulatory controllers. Part II: stability analysis, tuning rules and experimental validation. J. Process Control 19(2), 272–287 (2009)CrossRefGoogle Scholar
  9. 9.
    Anbarasan, K.; Srinivasan, K.: Design of RTDA controller for industrial process using SOPDT model with minimum or non-minimum zero. ISA Trans. 57, 231–244 (2015)CrossRefGoogle Scholar
  10. 10.
    Guan, S.T.; Chu, J.Z.: Research and application of RTD-A controller for multiple input multiple output (MIMO) system. Inf. Control 37(4), 429–434 (2008)Google Scholar
  11. 11.
    Chu, J.Z.; Du, B.; Chen, J.: Performance analysis and online fuzzy self-tuning of RTD-A controller’s parameters. J. Shanghai Jiaotong Univ. 45(8), 1167–1171 (2011)Google Scholar
  12. 12.
    Sendjaja, A.Y.; Zhen, F.N.; Si, S.H.; Kariwala, V.: Analysis and tuning of RTD-A controllers. Ind. Eng. Chem. Res. 50(6), 3415–3425 (2011)CrossRefGoogle Scholar
  13. 13.
    Luan, X.L.; Wang, Z.Q.; Liu, F.: Centralized PI control for multivariable non-square systems. Control Decis. 31(5), 811–816 (2016)MATHGoogle Scholar
  14. 14.
    Davison, E.J.: Some properties of minimum phase systems and "squared-down" systems. IEEE Trans. Autom. Control 28(2), 221–222 (1984)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Treiber, S.: Multivariable control of non-square systems. Ind. Eng. Chem. Process Des. Dev. 23(4), 854–857 (1984)CrossRefGoogle Scholar
  16. 16.
    He, M.J.; Cai, W.J.; Wei, N.; Xie, L.H.: RNGA based control system configuration for multivariable processes. J. Process Control 19(6), 1036–1042 (2009)CrossRefGoogle Scholar
  17. 17.
    Zou, T.; Li, H.Q.; Ding, B.C.; Wang, D.D.: Compatibility and uniqueness analyses of steady state solution for multi-variable predictive control systems. Acta Autom. Sin. 39(5), 519–529 (2013)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Jin, Q.B.; Hao, F.; Wang, Q.: A multivariable IMC-PID method for non-square large time delay systems using NPSO algorithm. J. Process Control 23(5), 649–663 (2013)CrossRefGoogle Scholar
  19. 19.
    Jin, Q.B.; Liu, Q.: Decoupling proportional-integral-derivative controller design for multivariable processes with time delays. Ind. Eng. Chem. Res. 53(2), 765–777 (2014)CrossRefGoogle Scholar
  20. 20.
    Jin, Q.B.; Du, X.H.; Wang, Q.; Liu, L.Y.: Analytical design 2 DOF IMC control based on inverted decoupling for non square systems with time delay. Can. J. Chem. Eng. 94(7), 1354–1367 (2016)CrossRefGoogle Scholar
  21. 21.
    Yang, S.H.; Wang, X.Z.; Mcgreavy, C.: A multivariable coordinated control system based on predictive control strategy for FCC reactor-regenerator system. Chem. Eng. Sci. 51(11), 2977–2982 (1996)CrossRefGoogle Scholar
  22. 22.
    Zou, T.; Wang, D.; Pan, H.; Yuan, M.; Ji, Z.: From zone model predictive control to double-layered model predictive control. Ciesc J. 64(12), 4474–4483 (2013)Google Scholar
  23. 23.
    Prett, D.M.; García, C.E.; Ramaker, B.L.: The second shell process control workshop. In: The Shell Process Control Workshop, Butterworths, pp. 325–347 (1987)Google Scholar
  24. 24.
    Vlachos, C.; Williams, D.; Gomm, J.B.: Solution to the shell standard control problem using genetically tuned PID controllers. Control Eng. Pract. 10(2), 151–163 (2002)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.College of Information Science and TechnologyBeijing University of Chemical TechnologyBeijingChina

Personalised recommendations