Abstract
This paper is concerned with a nonlinear Dynamic Matrix Control (DMC) algorithm in which measured disturbances are compensated. Neural networks are used to calculate on-line step response coefficients for the current operating point. Such models are obtained easily off-line, no recurrent training is necessary. The algorithm is computationally efficient since the optimal future control policy is determined on-line from an easy to solve quadratic programming problem and the model is not linearised on-line. It is shown that when applied to a significantly nonlinear process the algorithm offers good control accuracy (both trajectory tracking and disturbance compensation tasks are considered).
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)
Cutler, C.R., Ramaker, B.L.: Dynamic matrix control – a computer control algorithm. In: Proceedings of the Joint Automatic Control Conference, San Francisco, USA (1979)
Haykin, S.: Neural networks – a comprehensive foundation. Prentice Hall, Englewood Cliffs (1999)
Henson, M.A.: Nonlinear model predictive control: current status and future directions. Computers and Chemical Engineering 23, 187–202 (1998)
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)
Marusak, P.: Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors. Applied Soft Computing 9, 1111–1125 (2009)
Nørgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural networks for modelling and control of dynamic systems. Springer, London (2000)
Peng, H., Yang, Z.J., Gui, W., Wu, M., Shioya, H., Nakano, K.: Nonlinear system modeling and robust predictive control based on RBF-ARX model. Engineering Applications of Artificial Intelligence 20, 1–9 (2007)
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)
Zamarreño, J.M., Vega, P., Garcia, L.D., Francisco, M.: State-space neural network for modelling, prediction and control. Control Engineering Practice 8, 1063–1075 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ławryńczuk, M. (2010). Neural Dynamic Matrix Control Algorithm with Disturbance Compensation. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13033-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-13033-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13032-8
Online ISBN: 978-3-642-13033-5
eBook Packages: Computer ScienceComputer Science (R0)