PLC based model predictive control for industrial process control

Aslam, et al.

International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 6 (June 2017), Pages: 63-71

Title: PLC based model predictive control for industrial process control

Author(s): Sohaib Aslam *, SundasHannan, Muhammad Umar Sajjad, Muhammad Waheed Zafar

Affiliation(s):

Department of Electrical Engineering, Faculty of Engineering and Technology, Superior University, Lahore, Pakistan

https://doi.org/10.21833/ijaas.2017.06.009

Full Text - PDF XML

Abstract:

This paper presents the real-time implementation of model predictive control technique on programmable logic controller for industrial process control. Firstly, temperature of water is regulated in liquid tank at desired temperature and then level of water in filling bottles is controlled through PWM based flow control valve. The simulations of MPC for temperature and level control are presented in MATLAB by first developing the system model of water tank. The simulation results have shown that MPC effectively regulates both temperature and level of water. Finally, MPC is real time implemented on PLC and provides better real-time control of these two variables.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Programmable logic controller, Model predictive control, Pulse width modulation, Liquid tank, State-space model

Article History: Received 3 January 2017, Received in revised form 24 March 2017, Accepted 31 March 2017

Digital Object Identifier:

https://doi.org/10.21833/ijaas.2017.06.009

Citation:

Aslam S, Hannan S, Sajjad MU, and Zafar MW (2017). PLC based model predictive control for industrial process control. International Journal of Advanced and Applied Sciences, 4(6): 63-71

http://www.science-gate.com/IJAAS/V4I6/Aslam.html

References:

Aslam S, Hannan S, and Haider A (2016b). Effect of laguerre function parameters on MPC performance for speed control of a DC motor. Journal of Control Engineering and Technology, 6(1): 1-13.

Aslam S, Hannan USS, and Zafarm W (2016a). Implementation of PID on PIC24F series microcontroller for speed control of a DC motor using MPLAB and Proteus. Advances in Science and Technology Research Journal, 10(31): 40-50. https://doi.org/10.12913/22998624/64060

Aslam S, Hannan USS, and Zafarm W (2016c). Temperature control of water-bath system in presence of constraints by using MPC. International Journal of Advanced and Applied Sciences, 3(12): 62-68. https://doi.org/10.21833/ijaas.2016.12.009

Colla M, Leidi T, and Semo M (2009). Design and implementation of industrial automation control systems: A survey. In the IEEE 7th International Conference on Industrial Informatics. IEEE, Wales, UK: 570-575. https://doi.org/10.1109/indin.2009.5195866

Huyck B, Ferreau HJ, Diehl M, De Brabanter J, Van Impe JF, De Moor B, and Logist F (2012). Towards online model predictive control on a programmable logic controller: Practical considerations. Mathematical Problems in Engineering, 2012: Article ID 912603, 20 pages. https://doi.org/10.1155/2012/912603

Lashin MM (2014). Different applications of programmable logic controller (PLC). International Journal of Computer Science, Engineering and Information Technology, 4(1): 27-32.

Li SE, Jia Z, Li K, and Cheng B (2013). Scale reduction based efficient model predictive control and its application in vehicle following control. In the IEEE 16th International Conference on Intelligent Transportation Systems, IEEE, The Hague, Netherlands: 1266-1271. https://doi.org/10.1109/ITSC.2013.6728405

Liuping (2009). Model predictive control system design and implementation using MATLAB. Springer Science & Business Media, London, UK.

Maciejowski J (2002). Predictive control: With constraints, Prentice Hall, New Jersey, USA. .

Netto R and Bagri A (2013). Programmable logic controllers. International Journal of Computer Applications, 77(11): 27-31. https://doi.org/10.5120/13439-1291

Qin SJ and Badgwell TA (2003). A survey of industrial model predictive control technology. Control Engineering Practice, 11(7): 733-764. https://doi.org/10.1016/S0967-0661(02)00186-7

Rathore RS, Sharma AK, and Dubey HK (2015). PLC based PID implementation in process control of temperature flow and level. International Journal of Advanced Research in Engineering and Technology, 6(1): 9-26.