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access icon free Direct adaptive model predictive control tuning based on the first-order plus dead time models

  • Author(s): Tahereh Gholaminejad 1 ; Ali Khaki-Sedigh 1 ; Peyman Bagheri 2
    • View affiliations
  • Source: Volume 11, Issue 16, 03 November 2017, p. 2858 – 2869
    DOI:  10.1049/iet-cta.2016.1174 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 13/09/2016, Accepted 21/08/2017, Revised 28/04/2017, Published 22/08/2017

A direct adaptive tuning strategy is proposed for model predictive controllers. Parameter tuning is essential for a satisfactory control performance. Various tuning methods are proposed in the literature which can be categorised as heuristic, numerical and analytical methods. The proposed tuning methodology is based on an analytical model predictive control tuning approach for plants described by first-order plus dead time models. For a fixed tuning scheme, the tuning performance deteriorates in dealing with unknown or time varying plants. To overcome this problem, an adaptive tuning strategy is utilised. It is suggested to employ a discrete-time model reference adaptive control with recursive least squares estimations for controller tuning. The proposed method is also extended to multivariable systems. The stability and convergence of the proposed strategy is proved using the Lyapunov approach. Finally, simulation and experimental studies are used to show the effectiveness of the proposed methodology.

Inspec keywords: multivariable systems; predictive control; least squares approximations; Lyapunov methods; model reference adaptive control systems; discrete time systems; control system synthesis

Other keywords: controller tuning; first-order plus dead time models; direct adaptive tuning strategy; Lyapunov approach; discrete-time model reference adaptive control; multivariable systems; recursive least squares estimations; direct adaptive model predictive control tuning

Subjects: Control system analysis and synthesis methods; Self-adjusting control systems; Discrete control systems; Stability in control theory; Interpolation and function approximation (numerical analysis); Multivariable control systems; Optimal control

References

    1. 1)
      • 11. Ali, E., Al-Ghazzawi, A.: ‘On-line tuning of model predictive controllers using fuzzy logic’, Can. J. Chem. Eng., 2003, 81, (5), pp. 1041–1051.
    2. 2)
      • 24. Henson, M.A., Seborg, D.E.: ‘Adaptive nonlinear control of a pH neutralization process’, IEEE Trans. Control Syst. Technol., 1994, 2, (3), pp. 169–182.
    3. 3)
      • 15. Tran, Q.N., Scholten, J., Ozkan, L., et al: ‘A model-free approach for auto-tuning of model predictive control’. Preprints of the 19th World congress, The Int. Federation of Automatic Control, Cape Town, South Africa, January 2014, pp. 2189–2194.
    4. 4)
      • 1. Kano, M., Ogawa, M.: ‘The state of the art in chemical process control in Japan: good practice and questionnaire survey’, J. Process. Control., 2010, 20, (9), pp. 969–982.
    5. 5)
      • 23. Lynch, B., Dumont, G.A.: ‘Control loop performance monitoring’, IEEE Trans. Control Syst. Technol., 1996, 4, (2), pp. 185–192.
    6. 6)
      • 19. Bagheri, P., Khaki-Sedigh, A.: ‘An analytical tuning approach to multivariable model predictive controllers’, J. Process Control, 2014, 24, (12), pp. 41–54.
    7. 7)
      • 17. Bordons, C., Camacho, E.F.: ‘A generalized predictive controller for a wide class of industrial processes’, IEEE Trans. Control Syst. Technol., 1998, 6, (3), pp. 372–387.
    8. 8)
      • 13. Van der Leea, J.H., Svrcekb, W.Y., Youngc, B.R.: ‘A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making’, ISA Trans.., 2008, 47, (1), pp. 53–59.
    9. 9)
      • 5. Di Cairano, S., Bemporad, A.: ‘Model predictive control tuning by controller matching’, IEEE Trans. Automat Contr., 2010, 55, (1), pp. 185–190.
    10. 10)
      • 16. Bagheri, P., Khaki-Sedigh, A.: ‘Analytical approach to tuning of model predictive control for first-order plus dead time models’, IET Control Theor. Appl., 2013, 7, (14), pp. 1806–1817.
    11. 11)
      • 22. Johansson, R.: ‘Global Lyapunov stability and exponential convergence of direct adaptive control’, Int. J. Control, 1989, 50, (3), pp. 859–869.
    12. 12)
      • 12. Ali, E.: ‘Heuristic on-line tuning for nonlinear model predictive controllers using fuzzy logic’, J. Process Control, 2003, 13, (5), pp. 383–396.
    13. 13)
      • 21. Shridhar, R., Cooper, D.J.: ‘A tuning strategy for unconstrained SISO model predictive control’, Ind. Eng. Chem. Res., 1997, 36, (3), pp. 729–746.
    14. 14)
      • 10. Al-Ghazzawi, A., Ali, E., Nouh, A., et al: ‘On-line tuning strategy for model predictive controllers’, J. Process Control, 2001, 11, (3), pp. 265–284.
    15. 15)
      • 2. Darby, M.L., Nikolaou, M.: ‘MPC: current practice and challenges’, Control Eng. Pract., 2012, 20, (4), pp. 328–342.
    16. 16)
      • 18. Bagheri, P., Khaki-Sedigh, A.: ‘Closed form tuning equations for model predictive control of first-order plus fractional dead time models’, Int. J. Control Autom. Syst., 2015, 13, (1), pp. 73–80.
    17. 17)
      • 3. Mayne, D.Q.: ‘Model predictive control: recent developments and future promise’, Automatica, 2014, 50, (12), pp. 2967–2986.
    18. 18)
      • 7. Bagheri, P., Khaki-Sedigh, A.: ‘Robust tuning of dynamic matrix controllers for first order plus dead time models’, Appl. Math. Model, 2015, 39, (22), pp. 7017–7031.
    19. 19)
      • 9. Gerkšiè, S., Pregelj, B.: ‘Tuning of a tracking multi-parametric predictive controller using local linear analysis’, IET Control Theory Appl., 2012, 6, (5), pp. 669–679.
    20. 20)
      • 8. He, N., Shi, D., Wang, J., et al: ‘Automated two-degree-of-freedom model predictive control tuning’, Ind. Eng. Chem. Res., 2015, 54, (38), pp. 10811–10824.
    21. 21)
      • 6. Tran, Q.N., Özkan, L., Backx, A.C.P.M.: ‘Generalized predictive control tuning by controller matching’. in Proc. American Control Conf. (ACC), Portland, USA, June 2014, pp. 4889–4894.
    22. 22)
      • 20. Akhtar, S., Bernstein, D.S.: ‘Lyapunov-stable discrete-time model reference adaptive control’, Int. J. Adapt. Control Signal Process, 2005, 19, (10), pp. 745–767.
    23. 23)
      • 4. Garriga, J.L., Soroush, M.: ‘Model predictive control tuning methods: a review’, Ind. Eng. Chem. Res., 2010, 49, (8), pp. 3505–3515.
    24. 24)
      • 14. Valencia-Palomoa, G., Rossiter, J.A.: ‘Programmable logic controller implementation of an auto-tuned predictive control based on minimal plant information’, ISA Trans.., 2011, 50, (1), pp. 92–100.
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