Issue
Korean Journal of Chemical Engineering,
Vol.39, No.4, 865-875, 2022
The Pareto optimal robust design of generalized-order PI controllers based on the decentralized structure for multivariable processes
This paper proposes an optimal tuning approach for designing robust generalized-order proportional integral (PI) controllers based on the multi-objective optimization problem for multivariable processes. Generalized-order means that the order of the integral term could be an integer order or a fractional one. Due to the sophistication of an MIMO process, the decentralized structure based on the simplified decoupling is addressed to reduce the full matrix controller (n2 controllers) to the diagonal form (n controllers). Multi-objective particle swarm optimization (MOPSO) is adopted to design a generalized-order PI controller for each diagonal element of the decoupled matrix. The objective functions are to minimize the integrated absolute error (IAE) for both servomechanism and regulator problems which are normally conflicting in terms of system performance. In the first stage, a Pareto front (PF) including the optimal solutions is obtained, then in the second stage, the most appropriate control parameters are chosen from the PF based on the maximum peak of the sensitivity function (Ms). The robustness stability of the whole system (the MIMO one) is finally evaluated to guarantee the applicability of the control structure. Some simulation examples in comparison with other well-known methods are presented to demonstrate the effectiveness of the proposed method.
[References]
  1. Truong NLV, Lee M, J. Chem. Eng. Jpn., 43, 196, 2010
  2. Truong NLV, Lee M, J. Chem. Eng. Jpn., 46, 279, 2013
  3. Chuong VL, Vu TNL, Truong NTN, Jung JH, Appl. Sci., 9, 2487, 2019
  4. Bialkowski WL, Pulp Pap., 11, 19, 1994
  5. Chen YQ, Petras I, Xue D, Fractional order control - a tutorial, American Control Conference (2009).
  6. Astrom KJ, Panagopoulos H, Hagglund T, Automatica, 34(5), 585, 1998
  7. Kim TH, Maruta I, Sugie T, Automatica, 44, 1104, 2008
  8. Vilanova R, Arrieta O, Ponsa P, ISA Trans., 81, 177, 2018
  9. Dastjerdi AA, Vinagre BM, Chen YQ, HosseinNia H, Annu. Rev. Control, 47, 51, 2019
  10. Padula F, Visioli A, J. Process Control, 21, 69, 2011
  11. Vu TNL, Lee M, ISA Trans., 52, 583, 2013
  12. Keyser RD, Muresan CI, Ionescu CM, ISA Trans., 62, 268, 2016
  13. Yumuk E, Guzelkaya M, Eksin I, ISA Trans., 91, 196, 2019
  14. Beschi M, Padula F, Visioli A, Control Eng. Practice, 60, 190, 2016
  15. Moradi M, J. Process Control, 24, 336, 2014
  16. Sánchez HS, Padula F, Visioli A, Vilanova R, ISA Trans., 66, 344, 2017
  17. Hajiloo A, Nariman-zadeh N, Moeini A, Mechatronics, 22, 788, 2012
  18. Pan I, Das S, Int. J. Electr. Power Energy Syst., 43, 393, 2012
  19. Morari M, Zafiriou E, Robust process control, Englewood Cliffs, Prentice Hall (1989).
  20. Skogestad S, Postlethwaithe I, Multivariable feedback control analysis and design, John Wiley & Sons (1996).
  21. Coello CAC, Lechuga MS, CEC'02 (Cat. No. 02TH8600), USA, 2, 1051 (2002)
  22. Coello CAC, Pulido GT, Lechuga MS, IEEE Trans. Evo. Comp., 8(3), 256, 2004
  23. Monje CA, Chen YQ, Vinagre BM, Xue DY, Feliu V, Fractional-order systems and controls, fundamentals and applications, Springer-Verlag, London (2010).
  24. Chuong VL, Vu TNL, Truong NTN, Jung JH, Appl. Sci., 9(23), 5262, 2019
  25. Ogunnaike BA, Lemaire JP, Morari M, Ray WH, AIChE J., 29, 632, 1983
  26. Ghosh S, Pan S, ISA Trans., 110, 117, 2021
  27. Shen Y, Cai WJ, Li S, Control Eng. Practice, 18(6), 652, 2010
  28. Khandelwal S, Detroja KP, J. Process Control, 96, 23, 2020