Fuzzy-2 deployment in indirect vector control and hybrid space vector modulation for a two-level inverter fed induction motor drive

Soft computing based expert systems have a pivotal role to play in designing standalone mobility systems such as unmanned aerial vehicles. The expert systems are designed by learning from functional data of a system, thereby framing rules without relying on apre-defined model. The use of type-2 fuzzy (fuzzy-2) controllers instead of traditional proportional-integral controllers (PICs) enhances the dynamic performance of an indirect vector controlled (IVC) induction motor (IM) in this paper. The error tracking path (ETP) of PIC is fixed for a given set of gain constants. With the use of IF-THEN rules, the Mamdani fuzzy-2 inference system (MF2IS) processes ambiguous data and produces membership functions that work on three-dimensional inputs. Thus, controller gain constants become unfixed; thereby response attains zero error rapidly with an unfixed ETP. It is well-known that changing the switching frequency of the voltage source inverter (VSI) increases switching losses for the VSI but reducing total harmonic distortion (THD) of the line current and voltage of the IM is an important trade-off. Hence, this issue is also addressed by replacing fuzzy-2 duty cycle controller (F2DCC) in place of conventional duty cycle controller. Simulation experiments are conducted using MATLAB/SIMULINK to evaluate the efficacy of IM. The experimental validation is done on a prototype using Sacrae Theologiae Magister (STM) processor for 1 horsepower IM.