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.