Abstract
This paper discusses a Model Predictive Control (MPC) structure for economic optimisation of nonlinear technological processes. It contains two parts: an MPC economic optimiser/constraint governor and an unconstrained MPC algorithm. Two neural models are used: a dynamic one for control and a steady-state one for economic optimisation. Both models are linearised on-line. As a result, an easy to solve on-line one quadratic programming problem is formulated. Unlike the classical multilayer control system structure, the necessity of repeating two nonlinear optimisation problems at each sampling instant is avoided.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blevins, T.L., Mcmillan, G.K., Wojsznis, M.W.: Advanced control unleashed, ISA (2003)
Brdys, M., Tatjewski, P.: Iterative algorithms for multilayer optimizing control. Imperial College Press, London (2005)
Chen, J., Huang, T.C.: Applying neural networks to on-line updated PID controllers for nonlinear process control. Journal of Process Control 14, 211–230 (2004)
Findeisen, W.M., Bailey, F.N., Brdyś, M., Malinowski, K., Tatjewski, P., Woźniak, A.: Control and coordination in hierarchical systems. J. Wiley and Sons, Chichester (1980)
Haykin, S.: Neural networks – a comprehensive foundation. Prentice Hall, Englewood Cliffs (1999)
Hussain, M.A.: Review of the applications of neural networks in chemical process control – simulation and online implmementation. Artificial Intelligence in Engineering 13, 55–68 (1999)
Kassmann, D.E., Badgwell, T.A., Hawkins, R.B.: Robust steady-state target calculation for model predictive control. AIChE Journal 46, 1007–1024 (2000)
Ławryńczuk, M., Tatjewski, P.: Efficient predictive control integrated with economic optimisation based on neural models. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS, vol. 5097, pp. 111–122. Springer, Heidelberg (2008)
Ławryńczuk, M.: Neural models in computationally efficient predictive control cooperating with economic optimisation. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4669, pp. 650–659. Springer, Heidelberg (2007)
Ławryńczuk, M.: A family of model predictive control algorithms with artificial neural networks. Int. Journal of Applied Mathematics and Computer Science 17, 217–232 (2007)
Maciejowski, J.M.: Predictive control with constraints. Prentice Hall, Englewood Cliffs (2002)
Nørgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural networks for modelling and control of dynamic systems. Springer, London (2000)
Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11, 733–764 (2003)
Saez, D., Cipriano, A., Ordys, A.W.: Optimisation of industrial processes at supervisory level: application to control of thermal power plants. Springer, Heidelberg (2002)
Tatjewski, P.: Advanced control of industrial processes, Structures and algorithms. Springer, London (2007)
Tatjewski, P., Ławryńczuk, M.: Soft computing in model-based predictive control. Int. Journal of Applied Mathematics and Computer Science 16, 101–120 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ławryńczuk, M. (2009). A Predictive Control Economic Optimiser and Constraint Governor Based on Neural Models. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-04921-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04920-0
Online ISBN: 978-3-642-04921-7
eBook Packages: Computer ScienceComputer Science (R0)