© The Institution of Engineering and Technology
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 firstorder 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 discretetime 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.
References


1)

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.

2)

2. Darby, M.L., Nikolaou, M.: ‘MPC: current practice and challenges’, Control Eng. Pract., 2012, 20, (4), pp. 328–342.

3)

3. Mayne, D.Q.: ‘Model predictive control: recent developments and future promise’, Automatica, 2014, 50, (12), pp. 2967–2986.

4)

4. Garriga, J.L., Soroush, M.: ‘Model predictive control tuning methods: a review’, Ind. Eng. Chem. Res., 2010, 49, (8), pp. 3505–3515.

5)

5. Di Cairano, S., Bemporad, A.: ‘Model predictive control tuning by controller matching’, IEEE Trans. Automat Contr., 2010, 55, (1), pp. 185–190.

6)

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.

7)

7. Bagheri, P., KhakiSedigh, A.: ‘Robust tuning of dynamic matrix controllers for first order plus dead time models’, Appl. Math. Model, 2015, 39, (22), pp. 7017–7031.

8)

8. He, N., Shi, D., Wang, J., et al: ‘Automated twodegreeoffreedom model predictive control tuning’, Ind. Eng. Chem. Res., 2015, 54, (38), pp. 10811–10824.

9)

9. Gerkšiè, S., Pregelj, B.: ‘Tuning of a tracking multiparametric predictive controller using local linear analysis’, IET Control Theory Appl., 2012, 6, (5), pp. 669–679.

10)

10. AlGhazzawi, A., Ali, E., Nouh, A., et al: ‘Online tuning strategy for model predictive controllers’, J. Process Control, 2001, 11, (3), pp. 265–284.

11)

11. Ali, E., AlGhazzawi, A.: ‘Online tuning of model predictive controllers using fuzzy logic’, Can. J. Chem. Eng., 2003, 81, (5), pp. 1041–1051.

12)

12. Ali, E.: ‘Heuristic online tuning for nonlinear model predictive controllers using fuzzy logic’, J. Process Control, 2003, 13, (5), pp. 383–396.

13)

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.

14)

14. ValenciaPalomoa, G., Rossiter, J.A.: ‘Programmable logic controller implementation of an autotuned predictive control based on minimal plant information’, ISA Trans.., 2011, 50, (1), pp. 92–100.

15)

15. Tran, Q.N., Scholten, J., Ozkan, L., et al: ‘A modelfree approach for autotuning 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.

16)

16. Bagheri, P., KhakiSedigh, A.: ‘Analytical approach to tuning of model predictive control for firstorder plus dead time models’, IET Control Theor. Appl., 2013, 7, (14), pp. 1806–1817.

17)

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.

18)

18. Bagheri, P., KhakiSedigh, A.: ‘Closed form tuning equations for model predictive control of firstorder plus fractional dead time models’, Int. J. Control Autom. Syst., 2015, 13, (1), pp. 73–80.

19)

19. Bagheri, P., KhakiSedigh, A.: ‘An analytical tuning approach to multivariable model predictive controllers’, J. Process Control, 2014, 24, (12), pp. 41–54.

20)

20. Akhtar, S., Bernstein, D.S.: ‘Lyapunovstable discretetime model reference adaptive control’, Int. J. Adapt. Control Signal Process, 2005, 19, (10), pp. 745–767.

21)

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.

22)

22. Johansson, R.: ‘Global Lyapunov stability and exponential convergence of direct adaptive control’, Int. J. Control, 1989, 50, (3), pp. 859–869.

23)

23. Lynch, B., Dumont, G.A.: ‘Control loop performance monitoring’, IEEE Trans. Control Syst. Technol., 1996, 4, (2), pp. 185–192.

24)

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.
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