Abstract
This paper deals with modeling and control of a hydraulic three tank system. A process of creating a computer model in MATLAB / Simulink environment is described and optimal PID and model predictive controllers are proposed. Modeling starts with creation of an initial mathematical model based on first principles approach. Further, the initial model is modified to obtain better correspondence with real-time system and parameters of the modified system are identified from measurements. The real time system contains nonlinearities which cannot be neglected and therefore are identified and included in the final mathematical model. Resulting model is used for control design. As the real-time system has long time constants, usage of Simulink model dramatically speeds up design process. Optimal PID and MPC controllers are proposed and compared. Described techniques are not limited to one particular modeling problem but can be used as an illustrative example for modeling of many technological processes.
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
Bobál, V., Böhm, J., Fessl, J., Macháček, J.: Digital Self-tuning Controllers: Algorithms, Implementation and Applications. Springer-Verlag London Ltd. (2005)
Liu, G.P.: Nonlinear identification and control – A neural network Approach. Springer-Verlag London Ltd., London (2001)
Ljung, L.: System identification: theory for the user. Prentice Hall PTR, Upper Saddle River (1999)
Himmelblau, D.M., Riggs, J.B.: Basic principles and calculations in chemical engineering. Prentice Hall, Upper Saddle River (2004)
Amira, DTS200 Laboratory Setup Three - Tank - System. Amira GmbH, Duisburg (2002)
Li, L., Zhou, D.: Fast and robust fault diagnosis for a class of nonlinear systems: detectability analysis. Computers & Chemical Engineering 28, 2635–2646 (2004)
Henry, D., Zolghadri, A.: Norm-based design of robust FDI schemes for uncertain system under feedback control: Comparison of two approaches. Control Engineering Practice 14, 1081–1097 (2006)
Humusoft, Real Time Toolbox. Humusoft, Praha, (2011), http://www.humusoft.cz/produkty/rtt/
Chalupa, P., Novák, J., Bobál, V.: Modeling of Hydraulic Control Valves. In: Proceedings of the 13th WSEAS International Conference (ACMOS 2011), Lanzarote, Canary Islands, Spain, May 27-29, pp. 195–200 (2011)
Chalupa, P., Novák, J., Bobál, V.: Comprehensive Model of DTS200 Three Tank System in Simulink. International Journal of Mathematical Models and Methods in Applied Sciences 6(2), 358–365 (2012)
Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E.: Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions. SIAM Journal of Optimization 9(1), 112–147 (1998)
Chalupa, P.: STuMPCoL Self-Tuning Model Predictive Controllers Library (2012), http://www.fai.utb.cz/people/chalupa/STuMPCoL
Kwon, W.H., Han, S.: Receding Horizon Control, 380 pages. Springer, London (2005) 978-1-84628-024-5
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chalupa, P., Novák, J. (2013). Modeling and Model Predictive Control of Nonlinear Hydraulic System. In: Zelinka, I., Rössler, O., Snášel, V., Abraham, A., Corchado, E. (eds) Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems. Advances in Intelligent Systems and Computing, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33227-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-33227-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33226-5
Online ISBN: 978-3-642-33227-2
eBook Packages: EngineeringEngineering (R0)