Design of QFT-Based Self-Tuning Deadbeat Controller

Authors: H. Mansor, S. B. Mohd Noor

Abstract:

This paper presents a design method of self-tuning Quantitative Feedback Theory (QFT) by using improved deadbeat control algorithm. QFT is a technique to achieve robust control with pre-defined specifications whereas deadbeat is an algorithm that could bring the output to steady state with minimum step size. Nevertheless, usually there are large peaks in the deadbeat response. By integrating QFT specifications into deadbeat algorithm, the large peaks could be tolerated. On the other hand, emerging QFT with adaptive element will produce a robust controller with wider coverage of uncertainty. By combining QFT-based deadbeat algorithm and adaptive element, superior controller that is called selftuning QFT-based deadbeat controller could be achieved. The output response that is fast, robust and adaptive is expected. Using a grain dryer plant model as a pilot case-study, the performance of the proposed method has been evaluated and analyzed. Grain drying process is very complex with highly nonlinear behaviour, long delay, affected by environmental changes and affected by disturbances. Performance comparisons have been performed between the proposed self-tuning QFT-based deadbeat, standard QFT and standard dead-beat controllers. The efficiency of the self-tuning QFTbased dead-beat controller has been proven from the tests results in terms of controller’s parameters are updated online, less percentage of overshoot and settling time especially when there are variations in the plant.

Keywords: Deadbeat control, quantitative feedback theory (QFT), robust control, self-tuning control.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087322

References:

[1] P. –O. Gutman, “Robust and adaptive control: fidelity or an open relationship,” Systems & Control Letters, vol. 49, pp. 9-19, 2003.
[2] K. K. Ahn and Q. T. Dinh, “Self-tuning of Quantitative feedback theory for force control of an electro-hydraulic test machine,” Control Engineering Practice, vol. 17, pp. 1291-1306, 2009.
[3] H. Mansor, S. B. M. Noor, R. K. R. Ahmad and F. S. Taip, “Online Quantitative Feedback Theory (QFT)-based self-tuning controller for grain drying process,” Scientific Research and Essays, vol.6 (30), pp. 6530-6534, 2011.
[4] P. J. Gawthrop, “Quantitative feedback theory and self-tuning control,” The Proceedings of the International Conference on Control, pp. 616- 621, 1988.
[5] P. S. V. Nataraj and N. Kubal, “Adaptive QFT control using hybrid global optimisation and constraint propagation techniques,” The Proceedings of the 47th IEEE Conference on Decision and Control, pp. 1001-1005, 2008.
[6] D. Q. Truong, K. K Ahn, and J. I. Yoon, “A study on a force control of electric-hydraulic load simulator using online tuning quantitative feedback theory,” Proceedings of the International Conference on Control, Automation and Systems, pp. 2622-2627, 2008.
[7] Barbargires, C. A. and Karybakas, C.A, “Minimum-energy ripple-free dead-beat control of type-I second-order plants,” Proceedings of ICECS ’99, The 6th IEEE International Conference on Electronics, Circuits and Systems, vol. 2, pp. 1163-1166, 1999.
[8] H. Mansor, S. Khan and T.S. Gunawan, “Modelling and control of laboratory scale conveyor belt type grain dryer plant,” Journal of Food, Agriculture & Environment, vol. 10(2), pp. 1384-1388, 2012.
[9] R. D. Whitfield, “Control of a mixed-flow drier part 2: test of the control algorithm,” Journal of Agricultural Engineering Research, vol. 41(4), pp. 289-299, 1988.
[10] C. Borghesani, Y. Chait and O. Yaniv, “QFT frequency domain control design toolbox: for use with Matlab”: Terasoft, Inc., 1999.
[11] V. Bobal and P. Chalupa, “Self-tuning controllers Simulink library,” Zlin: Thomas Bata University, 2008.