Design of supervisory based switching QFT controllers for improved closed loop performance

In this paper, the problem of supervisory based switching Quantitative Feedback Theory (QFT) control is proposed for the control of highly uncertain plants. According to this strategy, the uncertainty region is divided into smaller regions with a nominal model. It is assumed that a QFT controller exits for robust stability and performance of the individual uncertain sets. The proposed control architecture is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the candidate local model behavior with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching for stability reasons. It is shown that this strategy leads to improved closed loop performance, and can also handle the uncertainty sets that can not be tackled by a single QFT robust controller. Simulation results are proposed to show the effectiveness of the proposed methodology.