Dynamic Matrix Control and Tuning Parameters Analysis for a DC Motor System Control
Abstract
Model predictive control (MPC) in system control industry overrides the challenges of conventional controllers in controlling complex systems. However, for efficient control, it is essential to find the best combination of parameter values. In this paper, we present the implementation of a multivariable dynamic matrix control (DMC) algorithm. An industrial system consisting of a DC motor, coupled to a mechanical load, the assembly associated with an electronic speed variator was considered to test the implemented DMC controller. DMC’s tuning parameter analysis on the manipulated inputs and their variations on the controlled outputs was performed. Results guarantee that efficient control was presented.
Keywords:
model predictive control (MPC), dynamic matrix control (DMC), tuning parameters, DC motor controlDownloads
References
A. T. Boum, “Observer based and quadratic dynamic matrix control of a fluid catalytic cracking unit: A comparison study”, International Journal of Computer Applications, Vol. 80, No. 3, pp. 1-8, 2013 DOI: https://doi.org/10.5120/13838-1668
J. M. Lopez-Guede, B. Fernandez-Gauna, M. Grana, F. Oterino, “On the Influence of the Prediction Horizon in Dynamic Matrix Control”, International Journal of Control, Vol. 3, No. 1, pp. 22-30, 2013
E. F. Camacho, C. Bordons, Model Predictive Control in the Process Industry, Springer Science & Business Media, 2012
S. Joe Qin, T. A. Badgwell, “A survey of industrial model predictive control technology”, Control Engineering Practice, Vol. 11, No. 7, pp. 733-764, 2003 DOI: https://doi.org/10.1016/S0967-0661(02)00186-7
J. Richalet, A. Rault, J. Testud, J. Papon, “Model predictive heuristic control: Applications to industrial processes”, Automatica, Vol. 14, No. 5, pp. 413-428, 1978 DOI: https://doi.org/10.1016/0005-1098(78)90001-8
N. Vatsa, Tuning Parameters of Dynamic Matrix Control, PhD Thesis, National Institute of Technology Rourkela, India, 2011
G. M. de Almeida, M. A. de S. L. Cuadro, R. P. P. Amarai, J. L. F. Salles, “Optimal tuning parameters of the dynamic matrix predictive controller with ant colony optimization”, 11th IEEE/IAS International Conference on Industry Applications, Juiz de Fora, Brazil, December 7-10, 2014 DOI: https://doi.org/10.1109/INDUSCON.2014.7059396
P. Bagheri, A. K. Sedigh, “Robust tuning of dynamic matrix controllers for first order plus dead time models”, Applied Mathematical Modelling, Vol. 39, No. 22, pp. 7017-7031, 2015 DOI: https://doi.org/10.1016/j.apm.2015.02.035
A. S. Yamashita, A. C. Zanin, D. Odloak, “Tuning of model predictive control with multi-objective optimization”, Brazilian Journal of Chemical Engineering, Vol. 33, No. 2, pp. 333-346, 2016 DOI: https://doi.org/10.1590/0104-6632.20160332s20140212
D. Dougherty, D. J. Cooper, “Tuning guidelines of a dynamic matrix controller for integrating (non-self-regulating) processes”, Industrial & Engineering Chemistry Research, Vol. 42, No. 8, pp. 7039-7052, 2003 DOI: https://doi.org/10.1021/ie020546p
C. M. Reverter, J. Ibarrola, J. M. Cano-Izquierdo, “Tuning rules for a quick start up in Dynamic Matrix Control”, ISA Transactions, Vol. 53, No. 2, pp. 612-627, 2014 DOI: https://doi.org/10.1016/j.isatra.2013.12.012
P. Acharya, Performance Analysis of Model Predictive Control For Distillation Column, PhD Thesis, National Institute of Technology Rourkela, India, 2016
R. E. Kalman, “Contributions to the theory of optimal control”, Boletin de la Sociedad Matematica Mexicana, Vol. 5, No. 2, pp. 102-119, 1960
C. R. Cutler, B. L. Ramaker, “Dynamic matrix control-a computer control algorithm”, The National Meeting of the American Institute of Chemical Engineers, Houston, USA, April 1979
N. R. Ruchika, “Model predictive control: History and development”, International Journal of Engineering Trends and Technology, Vol. 4, No. 6, pp. 2600-2602, 2013
R. Shridhar, D. J. Cooper, “A novel tuning strategy for multivariable model predictive control”, ISA Transactions, Vol. 36, No. 4, pp. 273-280, 1998 DOI: https://doi.org/10.1016/S0019-0578(97)00036-0
C. R. Cutler, Dynamic Matrix Control, An Optimal Multivariable Control Algorithm with Constraints, PhD Thesis, University of Houston, USA, 1983
L. Bitjoka, M. Ndje, A. T. Boum, J. Song-Manguelle, “Implementation of quadratic dynamic matrix control on arduino due ARM cortex-M3 microcontroller board”, Journal of Engineering Technology, Vol. 6, No. 2, pp. 682-695, 2017
A. A. Kheriji, F. Bouani, M. Ksouri, M. B. Ahmed, “A microcontroller implementation of model predictive control”, International Journal of Electrical and Information Engineering, Vol. 5, No. 5, pp. 600-606, 2011
A. K. Abbes, F. Bouani, M. Ksouri, “A microcontroller implementation of constrained model predictive control”, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 5, No. 8, pp. 878-885, 2011
C. Ekaputri, A. Syaichu-Rohman, “Model predictive control (MPC) design and implementation using algorithm-3 on board SPARTAN 6 FPGA SP605 evaluation kit”, IEEE 3rd International Conference on Instrumentation Control and Automation, Ungasan, Indonesia, August 28-30, 2013 DOI: https://doi.org/10.1109/ICA.2013.6734056
L. Malouche, A. K. Abbes, F. Bouani, “Automatic model predictive control implementation in a high-performance microcontroller”, IEEE 12th International Multi-Conference on Systems, Signals & Devices, Mahdia, Tunisia, March 16-19, 2015 DOI: https://doi.org/10.1109/SSD.2015.7348173
R. Nagarajan, S. Sathishkumar, S. Deepika, G. Keerthana, J. K. Kiruthika, R. Nandhini, “Implementation of Chopper Fed Speed Control of Separately Excited DC Motor Using PI Controller”, International Journal of Engineering and Computer Science, Vol. 6, No. 3, pp. 20631-20633, 2017 DOI: https://doi.org/10.18535/ijecs/v6i3.42
V. H. Haji, C. A. Monje, “Fractional-order PID control of a chopper-fed DC motor drive using a novel firefly algorithm with dynamic control mechanism”, Soft Computing, Vol. 22, No. 18, pp. 6135-6146, 2018 DOI: https://doi.org/10.1007/s00500-017-2677-5
S. Li, K. Y. Lim, D. G. Fisher, “A state space formulation for model predictive control”, AIChE Journal, Vol. 35, No. 2, pp. 241-249, 1989 DOI: https://doi.org/10.1002/aic.690350208
E. F. Camacho, C. Bordons, Model Predictive Control, Springer-Verlag, 1998 DOI: https://doi.org/10.1007/978-1-4471-3398-8_2
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