Offset-Free Model Predictive Control of an Open Water Channel Based on Moving Horizon Estimation
Publication: Journal of Irrigation and Drainage Engineering
Volume 143, Issue 3
Abstract
Open water systems such as irrigation canals are used to transport and deliver water from the source to the user. Water loss in these systems by seepage, leakage, evaporation, or unknown water offtakes can be large. If this loss is unknown to the model used, it will not be considered by the controller and create a real system model mismatch. This mismatch will affect the water level directly and create an offset from the reference set point of the water level. A control configuration for open water canals, model predictive control (MPC) based on moving horizon estimation (MHE-MPC), to deal with offset problems resulting from real system-model mismatch is described in this paper. MHE uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and systematically removes the offset in the controlled water level. This control configuration is numerically tested on an accurate hydrodynamic model of the Control Algorithms Test Canal of the Technical University of Catalonia (UPC-PAC). The results presented in this paper show that MHE-MPC can realize offset-free control and the results are better than those of the well-known disturbance modelling offset-free control scheme.
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Acknowledgments
The first draft of this paper was prepared with Assoc. Prof. Dr. ir. Peter-Jules van Overloop with his great support. The authors appreciate his deep kindness. The authors are also grateful to the anonymous reviewers for their valuable comments on the paper.
References
Aydin, B. E., van Overloop, P. J., and Tian, X. (2014). “Offset free model predictive control of an open water reach.” Int. Conf. on Hydroinformatics, CUNY Academic Works, New York.
Breckpot, M., and Agudelo, O. (2012). “Control of a single reach with model predictive control.” River Flow 2012, Proc., Int. Conf. on Fluvial Hydraulics, CRC Press, Boca Raton, FL, 1021–1028.
Camacho, E., and Bordons, C. (2007). Model predictive control, Springer, London.
Horváth, K., Galvis, E., Valentín, M. G., and Rodellar, J. (2015). “New offset-free method for model predictive control of open channels.” Control Eng. Pract., 41, 13–25.
Huang, R., Biegler, L. T., and Patwardhan, S. C. (2010). “Fast offset-free nonlinear model predictive control based on moving horizon estimation.” Ind. Eng. Chem. Res., 49(17), 7882–7890.
Irrigation Training and Research Center. (2013). “Canal automation model.” California Polytechnic State Univ., San Luis Obispo, CA.
Kinzli, K. D., Martinez, M., Oad, R., Prior, A., and Gensler, D. (2010). “Using an ADCP to determine canal seepage loss in an irrigation district.” Agric. Water Manage., 97(6), 801–810.
Kühl, P., Diehl, M., Kraus, T., Schlöder, J. P., and Bock, H. G. (2011). “A real-time algorithm for moving horizon state and parameter estimation.” Comput. Chem. Eng., 35(1), 71–83.
Maciejowski, J. M. (2002). Predictive control with constraints, Prentice Hall, London.
Maeder, U., Borrelli, F., and Morari, M. (2009). “Linear offset-free model predictive control.” Automatica, 45(10), 2214–2222.
Maeder, U., and Morari, M. (2010). “Offset-free reference tracking with model predictive control.” Automatica, 46(9), 1469–1476.
Martin Sânchez, J., and Rodellar, J. (1996). Adaptive predictive control: From the concepts to plant optimization, Prentice-Hall, London.
MATLAB version 8.6.0. [Computer software]. MathWorks, Natick, MA.
Muske, K. R., and Badgwell, T. A. (2002). “Disturbance modeling for offset-free linear model predictive control.” J. Process Control, 12(5), 617–632.
Pannocchia, G., and Rawlings, J. B. (2003). “Disturbance models for offset-free model-predictive control.” AIChE J., 49(2), 426–437.
Rao, C., Rawlings, J., and Lee, J. (2001). “Constrained linear state estimation—A moving horizon approach.” Automatica, 37(10), 1619–1628.
Rao, C. V., Wright, S. J., and Rawlings, J. B. (1998). “Application of interior-point methods to model predictive control.” J. Optim. Theory Appl., 99(3), 723–757.
Rawlings, J. B., and Mayne, D. Q. (2013). Model predictive control: Theory and design, Nob Hill Publishing, Madison, WI.
Rijo, M., and Almeida, A. B. (1993). “Performance of an automatic upstream controlled irrigation system: Conveyance efficiencies.” Irrig. Drain. Syst., 7(3), 161–172.
Rijo, M., and Pereira, L. S. (1987). “Measuring conveyance efficiencies to improve irrigation water management.” Irrig. Drain. Syst., 1(3), 267–276.
Schuurmans, J. (1997). “Control of water levels in open channels.” Ph.D. dissertation, Delft Univ. of Technology, Delft, Netherlands.
Sepúlveda Toepfer, C. (2008). “Instrumentation, model identification and control of an experimental irrigation canal.” Ph.D. dissertation, Universitat Politècnica de Catalunya, Barcelona, Spain.
van Overloop, P. J. (2006). “Model predictive control on open water systems.” Ph.D. dissertation, Delft Univ. of Technology, Delft, Netherlands.
van Overloop, P. J., Horváth, K., and Aydin, B. E. (2014). “Model predictive control based on an integrator resonance model applied to an open water channel.” Control Eng. Pract., 27, 54–60.
Wagenpfeil, J., Arnold, E., Linke, H., and Sawodny, O. (2013). “Modelling and optimized water management of artificial inland waterway systems.” J. Hydroinform., 15(2), 348–365.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 143 • Issue 3 • March 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 20, 2015
Accepted: Apr 18, 2016
Published online: Jul 18, 2016
Discussion open until: Dec 18, 2016
Published in print: Mar 1, 2017
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