Implementation of Adaptive Embedded Controller for a Temperature Process

  • J. Satheesh Kumar Department of Electronics and Instrumentation, Dayananda Sagar College of Engineering, Bangalore, India
  • Deepu Sankar Department of Electronics and Instrumentation, Karunya University, India
Keywords: embedded, control, MATLAB, Simulink, MRAC

Abstract

The paper proposed and carried out an adaptive embedded control strategy with the help of Arduino open hardware platform. The proposed control strategy is to carry out a cost-effective interface between the simulation software and a real-time process. The data acquisition and control is done with the help of Arduino Uno which has been interfaced with MATLAB Simulink The control algorithms developed in Simulink model can be downloaded into the Arduino Uno, working as a standalone controller. In this paper, various control algorithms are used to control the temperature process, including embedded Modified Model Reference Adaptive Control (MMRAC). Its performance is compared to other control algorithms. The result shows that the MMRAC scheme improves the transient performance of the temperature control system.

References

T. Mushiri, A. Mahachi, and C. Mbohwa, “A model reference adaptive control system for the pneumatic valve of the bottle washer in beverages using Simulink,” Proc. International Conference on Sustainable Materials Processing and Manufacturing (SMPM 17), Elsevier, January 2017, pp. 364-373.

B. M. Mirkin and Per-Olof Gutman, “Output feedback model reference adaptive control for multi-input-multi-output plants with state delay,” Systems & Control Letters, vol. 54, pp. 961-972, February 2005.

R. A. Fahmy, R. I. Badr, and Farouk A. Rahman, “Adaptive PID controller Using RLS for SISO stable and unstable systems,” Advances in Power Electronics, vol. 2014, pp. 1-5, October 2014.

R. A. Fahmy, R. I. Badr, and Farouk A. Rahman, “Adaptive PID controller Using RLS for SISO stable and unstable systems,” Advances in Power Electronics, vol. 2014, pp. 1-5, October 2014.

K. K. McKee, Gareth L. Forbes, Ilyas Mazhar, Rodney Entwistle, and Ian Howard, “Low cost remote data acquisition system, ”Curtin University, Department of Mechanical Engineering, Technical Note, December 2013.

S. Humayun, Maria Mehmood, and Faran Mahmood, “Developing a LabVIEW and MATLAB-based test bed for data acquisition, analysis and calibration of frequency generators over GPIB,” International Journal of Computer Applications, vol. 40, pp. 11-15, February 2012.

Handbook of Networked and Embedded Control Systems. Birkhauser Boston, University of Maryland, 2008.

D. Hercog, A. Rojko, M. Curkovic, B. Gergic, and K. Jezernik, “Embedded platform for rapid implementation of local and remote motion control experiments,” Przeglad Elektrotechniczny, vol. 87, pp. 73-76, March 2011.

M. Engin, “Design and control applications of mechatronic systems in engineering,” 1st Ed. London: Intec, 2017.

A. M. El-Nagar, “Embedded intelligent adaptive PI controller for an electromechanical system,” ISA Transactions, vol. 64, pp. 314-327, September 2016.

Ioana Nascu, Ioan Nascu, and G.Vlad, “Predictive adaptive control of an activated sludge wastewater treatment process,” Advances in Technology Innovation, vol. 1, pp. 38-40, March 2016.

M. P. R. V. Rao, “New design of model reference adaptive control systems,” Journal of Applied Mechanical Engineering, vol. 3, pp. 1-3, January 2014.

S.H. Rajani, B. M. Krishna, and U. Nair, “Adaptive and modified adaptive control for pressure regulation in a hypersonic wind tunnel,” International Journal of Modelling Identification and Control, vol. 29, pp. 78-87, March 2018.

K. J. Astrom and B. Wittenmark, “Adaptive control,” 2nd Ed. New York: Dover Publications Inc., 2013.

S. Zhang and S. Dian, “Controller design for compound pendulum with PID and MRAC switch control,” Proc. International Conference on Automation Control and Robotics Engineering (CACRE 2018), IOP Publishing, July 2018, pp. 1-5.

R.J. Pawar and B.J. Parvat, “MRAC and modified MRAC controller design for level process control,” Proc. Indian Control Conference (ICC 18), IEEE Explore, January 2018, pp. 217-222.

L. Guessas and K. Benmahammed, “Adaptive backstepping and PID optimized by genetic algorithm in control of chaotic,” International Journal of Innovative Computing Information and Control, vol. 7, pp. 5299-5312, September 2011.

Published
2019-03-18
How to Cite
[1]
J. S. Kumar and D. Sankar, “Implementation of Adaptive Embedded Controller for a Temperature Process”, Adv. technol. innov., vol. 4, no. 2, pp. 94-104, Mar. 2019.
Section
Articles