Comparison of Steady State and Dynamic Interaction Measurements in Multiloop Control Systems

https://doi.org/10.22146/ajche.50158

Renanto Handogo(1*), Avon T. H.(2), Joko Lelono(3)

(1) Department of Chemical Engineering Sepuluh Nopember Institute of Technology Campus ITS Sukolilo, Surabaya 60111 INDONESIA
(2) 
(3) 
(*) Corresponding Author

Abstract


The applicability of the steady-state Relative Gain Array (RGA) to measure dynamic process interactions in a multiloop control system was investigated. Several transfer function matrices were chosen, and the gains, time constants, and dead times of their elements were varied to represent the systems with dominant dynamic interactions. It was shown that the steady-state RGA method predicted the controller pairing accurately if the pairing elements recommended by RGA had the bigger gains and the same or smaller time constants compared to other elements in the corresponding rows. When these conditions were not met, the RGA would give a wrong result, and dynamic interaction measurements, such as the Average Dynamic Gain Array (ADGA) and the Inverse Nyquist Array (lNA), should be used instead to determine the best controller pairing in a multiloop control system. Keywords: Control pairing, dynamic process interaction, multiloop control systems, Relative Gain Array (RGA), and steady state.

Keywords


Control pairing, dynamic process interaction, multiloop control systems, Relative Gain Array (RGA), and steady state

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References

  1. Bristol, E.H. (1966). "On a new measure of interaction for multivariable process control," IEEE Trans Auto Control, 11, 133.
  2. Gagnepain, J. E., and Seborg, D.E. (1982). "Analysis of interaction with applications to multiloop control system design,"Ind. Eng. Chem. Process Des. Dev, 21, 5-11.
  3. Handogo, R., Wibawa, G., Rusmana, T., and Arief, H.M. (2004). "Steady-state and dynamic interaction analysis in multivariable control system," AJChE, 4, 1, 39-46.
  4. Luyben, M.L., and Luyben, W.L. (1997). Essentials of process control, McGraw-Hill, Inc.,New York.
  5. Luyben, W.L. (1986). "Simple method for tuning SISO controllers in multivariable systems,"Ind, Eng. Chem. Process Des. Dev., 25, 654-660.
  6. Marlin, T.E. (2000). Process control, designing processes and control systems for dynamic preformance, 2nd ed., McGraw-Hill, Inc., New York.
  7. Ogunnaike, B. A., and Ray. W. H. (1994). Process dynamics, modeling, and control (Topics in chemical engineering), Oxford University Press.
  8. Rosenbrock, H. H. (1969). "Design of multivariable control using inverse Nyquist array," Proc. IEEE, 116, 11, 319-325.
  9. Seborg, D.E., Edgar, T.T., and Mellichamp, D.A. (1989). Process dynamics and control. John Wiley and sons, Ltd., Mew York.
  10. Shinskey, F.G. (1996). Process control systems: Application, design, and tuning, 4th ed.,McGraw-Hill, Inc., New York.
  11. Witcher, M.F., and McAvoy, T.J.(1977)."Interaction control systems: Steady state and dynamic measuremet of interaction," ISA Trans.,16,3.



DOI: https://doi.org/10.22146/ajche.50158

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