State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints

Abstract

This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.

Share and Cite:

M. Ramzi, H. Youlal and M. Haloua, "State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints," Intelligent Control and Automation, Vol. 3 No. 1, 2012, pp. 50-58. doi: 10.4236/ica.2012.31007.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] N. Bennis, J. Duplaix, G. Enéa, M. Haloua and H. Youlal, “Greenhouse Climate Modelling and Robust Control,” Computers and Electronics in Agriculture, Vol. 61, No. 2, 2008, pp. 96-107. doi:10.1016/j.compag.2007.09.014
[2] M. Nachidi, F. Rodriguez, F. Tadeo and J. L. Guzmanb, “Takagi-Sugeno Control of Nocturnal Temperature in Greenhouses Using Air Heating,” ISA Transactions, Vol. 50, No. 2, 2011, pp. 315-320. doi:10.1016/j.isatra.2010.11.007
[3] R. F. Escobar, et al., “Sensor Fault Detection and Isolation via High-Gain Observers: Application to a DoublePipe Heat Exchanger,” ISA Transactions, Vol. 50, No. 3, 2011; pp. 480-486. doi:10.1016/j.isatra.2011.03.002
[4] M. F. Rahmat, N. A. Mohd Subha, K. M. Ishaq and N. Abdul Wahab, “Modeling and Controller Design for the VVS-400 Pilot Scale Heating and Ventillation System,” International Journal on Smart Sensing and Intelligent Systems, Vol. 2, No. 4, 2009, pp. 579-601.
[5] H. L. Ho, A. B. Rad, C. C. Chan and Y. K. Wong, “Comparative Studies of Three Adaptive Controllers,” ISA Transactions, Vol. 38, No. 1, 1999, pp. 43-53. doi:10.1016/S0019-0578(99)00004-X
[6] T. Kealy and A. O’Dwyer, “Closed Loop Identification of a First Order plus Dead Time Process Model under PI Control,” Proceedings of the Irish Signals and Systems Conference, University College, Cork, 25-26 June 2002, pp. 9-14.
[7] D. M. de la Pena, D. R. Ramirez, E. F. Camacho and T. Alamo, “Application of an Explicit Min-Max MPC to a Scaled Laboratory Process,” Control Engineering Practice, Vol. 13, No. 12, 2005, pp. 1463-1471. doi:10.1016/j.conengprac.2004.12.008
[8] R. Mooney and A. O’Dwyer, “A Case Study in Modeling and Process Control: The Control of a Pilot Scale Heating and Ventilation System,” Proceedings of the 23rd International Manufacturing Conference, University of Ulster, Jordanstown, August 2006, pp. 123-130.
[9] N. A. M. Subha, M. F. Rahmat and K. M. Ishaq, “Controller Design for a Pilot-Scale Heating and Ventilation System Using Fuzzy Logic Approach,” Jurnal Teknologi Keluaran Khas, Vol. 54, 2011, pp. 123-139.
[10] L. P. Wang, “Model Predictive Control System Design and Implementation Using MATLAB,” Springer, Berlin, 2009.
[11] L. Wang and P. C. Young, “An Improved Structure for Model Predictive Control Using Non-Minimal State Space Realisation,” Journal of Process Control, Vol. 16, No. 4, 2006, pp. 355-371. doi:10.1016/j.jprocont.2005.06.016
[12] J. M. Maciejowski, “Predictive Control with Constraints,” Prentice Hall, Upper Saddle River, 2002.
[13] A. Bemporad, F. Borrelli and M. Morari, “Model Predictive Control Based on Linear Programming the Explicit Solution,” IEEE Transactions on Automatic Control, Vol. 47, No. 12, 2002, pp. 1974-1985. doi:10.1109/TAC.2002.805688
[14] J. H. Lee and B. L. Cooley, “Min-Max Predictive Control Techniques for a Linear State-Space System with a Bounded Set of Input Matrices,” Automatica, Vol. 36, No. 3, 2000, pp. 463-473. doi:10.1016/S0005-1098(99)00178-8
[15] S. J. Qin, V. M. Martinez and B. A. Foss, “An Interpolating Model Predictive Control Strategy with Application to a Waste Treatment Plant,” Computers and Chemical Engineering, Vol. 21, No. 1, 1997, pp. S881-S886. doi:10.1016/S0098-1354(97)00160-9
[16] T. Kawabe, “Robust MPC Method for BMI Based Wheelchair,” Intelligent Control and Automation, Vol. 2, No. 2, 2011, pp. 340-350. doi:10.4236/ica.2011.24039
[17] P. V. Overschee and B. D. Moor, “N4sid: Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems,” Automatica, Vol. 30, No. 1, 1994, pp. 75-93. doi:10.1016/0005-1098(94)90230-5
[18] M. Verhagen, “Identification of the Deterministic Part of Mimo State Space Models Given in Innovations form from Input-Output Data,” Automatica, Vol. 30, No. 1, 1994, pp. 61-74. doi:10.1016/0005-1098(94)90229-1
[19] S. J. Qina, W. Lina and L. Ljung, “A Novel Subspace Identification Approach with Enforced Causal Models,” Automatica, Vol. 41, No. 12, 2005, pp. 2043-2053. doi:10.1016/j.automatica.2005.06.010
[20] M. Viberg, “Subspace-Based Methods for the Identification of Linear Time-Invariant Systems,” Automatica, Vol. 31, No. 12, 1995, pp. 1835-1852. doi:10.1016/0005-1098(95)00107-5
[21] M. Lovera, T. Gustafsson and M. Verhagen, “Recursive Subspace Identification of Linear and Nonlinear Wiener State Space Models,” Automatica, Vol. 36, No. 11, 2000, pp. 1639-1650. doi:10.1016/S0005-1098(00)00103-5
[22] T. C. S. Wibowo and N. Saad, “MIMO Model of an Interacting Series Process for Robust MPC via System Identification,” ISA Transactions, Vol. 49, No. 3, 2010, pp. 335-347. doi:10.1016/j.isatra.2010.02.005
[23] http://www.didalab-didactique.fr/2008/achat/produit_details.php?id=32&lng=FR
[24] E. Yesil, M. Guzelkaya, I. Eksin and O. A. Tekin, “Online Tuning of Set-Point Regulator with a Blending Mechanism Using PI Controller,” Turkish Journal of Electrical Engineering, Vol. 16, No. 2, 2008.
[25] P. J. Gawthrop and L. Wang, “Intermittent Predictive Control of an Inverted Pendulum,” Control Engineering Practice, Vol. 14, No. 11, 2006, pp. 1347-1356. doi:10.1016/j.conengprac.2005.09.002

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.