Stable and robust control strategy using interval-valued fuzzy systems

In this paper we propose an adaptive interval valued fuzzy controller for high performance direct vector-controlled induction motor drive. Interval valued controller compared with type-1 fuzzy controller has the advantage that it can take into account the linguistic uncertainties present in the rules of the estimated models. The proposed control framework consists of a mixture of two controllers: an interval valued fuzzy controller and a supervisory controller. The supervisory controller is used when the system starts to become unstable. The free parameters for the proposed controller are changed according to some learning rules based on Lyapunov stability. Simulation results on an induction motor show the effectiveness of the introduced method.