Abstract
This paper is concerned with a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on neural Wiener models. The model contains a linear dynamic part in series with a steady-state nonlinear part which is realised by a neural network. The model is linearised on-line, as a result the nonlinear MPC algorithm needs solving a quadratic programming problem. The algorithm gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimisation. In order to demonstrate accuracy and computational efficiency of the considered MPC algorithm, a polymerisation reactor is studied.
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
Bloemen, H.H.J., Chou, C.T., Boom, T.J.J., Verdult, V., Verhaegen, M., Backx, T.C.: Wiener model identification and predictive control for dual composition control of a distillation column. Journal of Process Control 11, 601–620 (2001)
Haykin, S.: Neural networks – a comprehensive foundation. Prentice Hall, Englewood Cliffs (1999)
Janczak, A.: Identification of nonlinear systems using neural networks and polynomial models. In: A block-oriented approach. Lecture Notes in Control and Information Sciences, vol. 310 (2005)
Kalafatis, A.D., Wang, L., Cluett, W.R.: Linearizing feedforward-feedback control of pH processes based on Wiener model. Journal of Process Control 15, 103–112 (2005)
Ławryńczuk, M.: A family of model predictive control algorithms with artificial neural networks. International Journal of Applied Mathematics and Computer Science 17, 217–232 (2007)
Maciejowski, J.M.: Predictive control with constraints. Prentice Hall, Englewood Cliffs (2002)
Maner, B.R., Doyle, F.J., Ogunnaike, B.A., Pearson, R.K.: Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models. Automatica 32, 1285–1301 (1996)
Morari, M., Lee, J.H.: Model predictive control: past, present and future. Computers and Chemical Engineering 23, 667–682 (1999)
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)
Tatjewski, P.: Advanced control of industrial processes, Structures and algorithms. Springer, London (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ławryńczuk, M. (2008). Suboptimal Nonlinear Predictive Control Based on Neural Wiener Models. In: Dochev, D., Pistore, M., Traverso, P. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2008. Lecture Notes in Computer Science(), vol 5253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85776-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-540-85776-1_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85775-4
Online ISBN: 978-3-540-85776-1
eBook Packages: Computer ScienceComputer Science (R0)