Abstract
Classical and modern controllers can satisfy a wide range of specifications when their internal parameters are selected correctly. A good balance between sensitivity, control effort and speed of response is generally the main objective of tuning. Numerous tuning guidelines for MPC have been proposed in the literature; many of them have been converted into suitable tuning rules and investigated with the help of simulations of processes. For instance, Yamuna and Unbehauens have published a study that resumes some of the tuning methods; for DMC and GPC approximations, available at the publication date of their paper [202], they used simulations of two transfer functions, a nonlinear unstable chemical reactor and a real-time laboratory turbo-generator control to evaluate the tuning methods. Garcia et al. have also addressed the tuning problem; in their work [203], they reviewed theorems and some general guidelines about tuning MPC. There are also, in the published literature, some guidelines or tuning discussion for specific MPC approximations, for instance GPC [141], DMC [204, 205] and MPC based on linear programming [176]. The MPC tuning is still an open research area and there are works recently published, for example [206, 207]. The guide presented in this chapter has the objective to resume the strategies used in this monograph to tune the PID and MPC.
This is a preview of subscription content, log in via an institution to check access.
References
Astrom, K.J., Hagglund, T.: PID Controllers: Theory, Design and Tuning, 2nd edn. ISA, Triangle (1995)
Rossiter, J.A.: Model-Based Predictive Control. CRC Press, Boca Raton (2003)
Peng, Y., Vrancic, D., Hanus, R.: Anti-windup, bumpless, and conditioned transfer techniques for PID controllers. IEEE Control Syst. Mag. 16, 48–57 (1996)
Bohn, C., Atherton, D.P.: An analysis package comparing PID anti-windup strategies. IEEE Control Syst. Mag. 15, 34–40 (1995)
Goodwin, G.C., Graebe, S.F., Salgado, M.E.: Control System Design. Prentice Hall, Upper Saddle River (2001)
Camacho, E.F., Bordons, C.: Model Predictive Control, 2nd edn. Springer, London (2004)
Clarke, D.W., Mohtadi, C., Tuffs, P.S.: Generalized predictive control (Part I: The basic algorithm). Automatica 23, 137–148 (1987)
Banerjee, P., Shah, S.L.: Tuning guidelines for robust generalized predictive control. In: Conference on Decision and Control, pp. 3233–3234 (1992)
Bemporad, A., Borrelli, F., Morari, M.: Model predictive control based on linear programming-the explicit solution. IEEE Trans. Autom. Control 47(12), 1974–1985 (2002)
Yamuna Rani, K., Unbehauens, H.: Study of predictive controller tuning methods. Automatica 33(12), 2243–2248 (1997)
Garcia, C.E., Prett, D.M., Morari, M.: Model predictive control: theory and practice a survey. Automatica 25(3), 335–348 (1989)
Shridhar, R., Cooper, D.J.: A tuning strategy for unconstrained SISO model predictive control. Ind. Eng. Chem. Res. 36, 729–746 (1997)
Shridhar, R., Cooper, D.J.: A novel tuning strategy for multivariable model predictive control. ISA Trans. 36(4), 273–280 (1998)
Wibowo, T.C.S., Saad, N., Karsiti, M.N.: A heuristic approach for tuning model predictive controller. Elektrika 11(1), 8–14 (2009)
Di Cairano, S., Bemporad, A.: Model predictive control tuning by controller matching. IEEE Trans. Autom. Control 55(1), 185–190 (2010)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this chapter
Cite this chapter
Munoz-Hernandez, G.A., Mansoor, S.P., Jones, D.I. (2013). Tuning Guidelines. In: Modelling and Controlling Hydropower Plants. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-2291-3_15
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2291-3_15
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2290-6
Online ISBN: 978-1-4471-2291-3
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)