A Microcontroller Implementation of Model Predictive Control
Model Predictive Control (MPC) is increasingly being
proposed for real time applications and embedded systems. However
comparing to PID controller, the implementation of the MPC in
miniaturized devices like Field Programmable Gate Arrays (FPGA)
and microcontrollers has historically been very small scale due to its
complexity in implementation and its computation time requirement.
At the same time, such embedded technologies have become an
enabler for future manufacturing enterprises as well as a transformer
of organizations and markets. Recently, advances in microelectronics
and software allow such technique to be implemented in embedded
systems. In this work, we take advantage of these recent advances
in this area in the deployment of one of the most studied and
applied control technique in the industrial engineering. In fact in
this paper, we propose an efficient framework for implementation
of Generalized Predictive Control (GPC) in the performed STM32
microcontroller. The STM32 keil starter kit based on a JTAG interface
and the STM32 board was used to implement the proposed GPC
firmware. Besides the GPC, the PID anti windup algorithm was
also implemented using Keil development tools designed for ARM
processor-based microcontroller devices and working with C/Cµ
langage. A performances comparison study was done between both
firmwares. This performances study show good execution speed and
low computational burden. These results encourage to develop simple
predictive algorithms to be programmed in industrial standard hardware.
The main features of the proposed framework are illustrated
through two examples and compared with the anti windup PID
controller.
[1] T. Alamo, D. Ramirez, and E. Camacho, "Efficient implementation of
constrained min-max model predictive control with bounded uncertainties:
a vertex rejection approach," Journal of Process Control, vol. 15,
pp. 149-158, 2005.
[2] E. Camacho and C. Bordons, Model Predictive Control. London:
Springer, 2004.
[3] G. D. Nicolao, L. Magni, and R. Scattolini, "Robust predictive control of
systems with uncertain impulse response," Automatica, vol. 32, no. 10,
pp. 1475-1479, 1996.
[4] P. Campo and M. Morari, "Robust model predictive control," in Proceedings
of American Control Conference, 1987, pp. 1021-1026.
[5] J. Rossiter, Model based predictive control: A practical approach.
Robert H.Bishop, 2005.
[6] M. Kothare, V. Balakrishnan, and M. Morari, "Robust constrained model
predictive control using linear matrix inequalities," Automatica, vol. 32,
no. 10, pp. 1361-1379, 1996.
[7] Y. Wang and J. Rawlings, "A new robust model predictive control
method 1: theory and computation," Journal of Process Control, vol. 14,
pp. 231-247, 2002.
[8] G. Pannochia, "Robust model predictive control with guaranteed set
point tracking," Journal of Process Control, vol. 14, pp. 927-937, 2004.
[9] B. Bouzouita, F. Bouani, and M. Ksouri, "Solving non convex minmax
predictive controller," in Proceedings of Information, Decision and
Control Conference, 2007.
[10] A. Kheriji, F. Bounani, and M. Ksouri, "Efficient implementation of
constraind robust model predictive control using a state space model,"
in Proceedings of International Conference on Informatics in Control,
Automation and Robotics (ICINCO), 2010.
[11] A. Kheriji, F. Bouani, and M. Ksouri, "Ggp approach to solve non
convex min-max robust model predictive controller for a class constrained
mimo systems," in Proceedings of the International Workshop
on Symbolic and Numerical Methods, Modeling and applications to
circuit design, (SM2ACD) CEDA competition, 2010.
[12] A. Kheriji, F. Bounani, and M. Ksouri, "A ggp approach to solve
non convex min-max predictive controller for a class of constrained
mimo systems described by state-space models," International Journal
of Control Automations and Systems (IJCAS), vol. 9, no. 3, 2011, will
appear in June.
[13] K. Ling, S. Yue, and J. Maciejowski, "A fpga implementation of model
predictive control," in Proceedings of American Control Conference,
2006.
[14] U. R. Y. Jayaraman, "Fpga implementation of predictive control strategy
for power factor correction," in Proceedings of World Academy of
Science, Engineering and Technology, 2008.
[15] K. Ling, B. Wu, and J. Maciejowski, "Embedded model predictive
control (mpc) using a fpga," in Proceedings of the 17th World Congress:
The International Federation of Automatic Control, Seoul, Korea, 2008.
[16] K. Watanabet, K. Ikeda, T. Fukuda, and S. Tzafestas, "Adaptive generalized
predictive control using a state space approach," in International
Workshop on Intelligent Robots and Systems IROS, Osaka, Japan, 1991.
[17] G. Palomo, K. Hilton, and J. Rossiter, "Predictive control implementation
in a plc using the iec 1131.3 programming standard," in Proceedings
of American Control Conference, 2009.
[1] T. Alamo, D. Ramirez, and E. Camacho, "Efficient implementation of
constrained min-max model predictive control with bounded uncertainties:
a vertex rejection approach," Journal of Process Control, vol. 15,
pp. 149-158, 2005.
[2] E. Camacho and C. Bordons, Model Predictive Control. London:
Springer, 2004.
[3] G. D. Nicolao, L. Magni, and R. Scattolini, "Robust predictive control of
systems with uncertain impulse response," Automatica, vol. 32, no. 10,
pp. 1475-1479, 1996.
[4] P. Campo and M. Morari, "Robust model predictive control," in Proceedings
of American Control Conference, 1987, pp. 1021-1026.
[5] J. Rossiter, Model based predictive control: A practical approach.
Robert H.Bishop, 2005.
[6] M. Kothare, V. Balakrishnan, and M. Morari, "Robust constrained model
predictive control using linear matrix inequalities," Automatica, vol. 32,
no. 10, pp. 1361-1379, 1996.
[7] Y. Wang and J. Rawlings, "A new robust model predictive control
method 1: theory and computation," Journal of Process Control, vol. 14,
pp. 231-247, 2002.
[8] G. Pannochia, "Robust model predictive control with guaranteed set
point tracking," Journal of Process Control, vol. 14, pp. 927-937, 2004.
[9] B. Bouzouita, F. Bouani, and M. Ksouri, "Solving non convex minmax
predictive controller," in Proceedings of Information, Decision and
Control Conference, 2007.
[10] A. Kheriji, F. Bounani, and M. Ksouri, "Efficient implementation of
constraind robust model predictive control using a state space model,"
in Proceedings of International Conference on Informatics in Control,
Automation and Robotics (ICINCO), 2010.
[11] A. Kheriji, F. Bouani, and M. Ksouri, "Ggp approach to solve non
convex min-max robust model predictive controller for a class constrained
mimo systems," in Proceedings of the International Workshop
on Symbolic and Numerical Methods, Modeling and applications to
circuit design, (SM2ACD) CEDA competition, 2010.
[12] A. Kheriji, F. Bounani, and M. Ksouri, "A ggp approach to solve
non convex min-max predictive controller for a class of constrained
mimo systems described by state-space models," International Journal
of Control Automations and Systems (IJCAS), vol. 9, no. 3, 2011, will
appear in June.
[13] K. Ling, S. Yue, and J. Maciejowski, "A fpga implementation of model
predictive control," in Proceedings of American Control Conference,
2006.
[14] U. R. Y. Jayaraman, "Fpga implementation of predictive control strategy
for power factor correction," in Proceedings of World Academy of
Science, Engineering and Technology, 2008.
[15] K. Ling, B. Wu, and J. Maciejowski, "Embedded model predictive
control (mpc) using a fpga," in Proceedings of the 17th World Congress:
The International Federation of Automatic Control, Seoul, Korea, 2008.
[16] K. Watanabet, K. Ikeda, T. Fukuda, and S. Tzafestas, "Adaptive generalized
predictive control using a state space approach," in International
Workshop on Intelligent Robots and Systems IROS, Osaka, Japan, 1991.
[17] G. Palomo, K. Hilton, and J. Rossiter, "Predictive control implementation
in a plc using the iec 1131.3 programming standard," in Proceedings
of American Control Conference, 2009.
@article{"International Journal of Electrical, Electronic and Communication Sciences:56747", author = "Amira Abbes Kheriji and Faouzi Bouani and Mekki Ksouri and Mohamed Ben Ahmed", title = "A Microcontroller Implementation of Model Predictive Control", abstract = "Model Predictive Control (MPC) is increasingly being
proposed for real time applications and embedded systems. However
comparing to PID controller, the implementation of the MPC in
miniaturized devices like Field Programmable Gate Arrays (FPGA)
and microcontrollers has historically been very small scale due to its
complexity in implementation and its computation time requirement.
At the same time, such embedded technologies have become an
enabler for future manufacturing enterprises as well as a transformer
of organizations and markets. Recently, advances in microelectronics
and software allow such technique to be implemented in embedded
systems. In this work, we take advantage of these recent advances
in this area in the deployment of one of the most studied and
applied control technique in the industrial engineering. In fact in
this paper, we propose an efficient framework for implementation
of Generalized Predictive Control (GPC) in the performed STM32
microcontroller. The STM32 keil starter kit based on a JTAG interface
and the STM32 board was used to implement the proposed GPC
firmware. Besides the GPC, the PID anti windup algorithm was
also implemented using Keil development tools designed for ARM
processor-based microcontroller devices and working with C/Cµ
langage. A performances comparison study was done between both
firmwares. This performances study show good execution speed and
low computational burden. These results encourage to develop simple
predictive algorithms to be programmed in industrial standard hardware.
The main features of the proposed framework are illustrated
through two examples and compared with the anti windup PID
controller.", keywords = "Embedded systems, Model Predictive Control, microcontroller,
Keil tool.", volume = "5", number = "5", pages = "650-7", }