Robust speed regulation of indirect vector control induction motor using fuzzy logic controllers based on optimization algorithms

Currently, in high-performance applications, the vector control (VC) scheme of induction motor (IM) is widely employed in industry. The VC scheme has significant features of decoupling torque and flux; also, its hardware implementation is easier. Conventionally, PID control schemes are frequently used for variable speed operation. However, the performance of the VC scheme is limited over a wide range of speed operation because of de-tuning caused by parameter uncertainties. To address the aforementioned challenging problem, adaptive and robust control strategies are mostly implemented. This paper presents various novel, adaptive, and robust control strategies, namely (a) fuzzy logic controller (FLC) based on Levenberg–Marquardt algorithm (LMA), (b) FLC based on steepest descent algorithm (SDA), (c) FLC based on Newton algorithm (NA), and (d) FLC based on Gauss–Newton algorithm (GNA) for the indirect vector control (IVC) three-phase IM. The focal motive is to accomplish fast dynamic response with fault-tolerant capability, load disturbance rejection qualities, insensitivity to the parameter uncertainties, robustness to speed variation, and to acquire maximum efficiency as well as torque. The d- q modeling of the IVC IM in the synchronous reference frame and space vector pulse width modulation (SVPWM) employed in inverter are designed in MATLAB/Simulink. Our work also presents critical, analytical, and comparative assessment of the proposed controllers with traditional tuned PI control strategy for the electrical faults perturbation, load disturbances, speed variations, and parameter uncertainties. Furthermore, the simulation results of the above-mentioned designed control strategies validated robust, smooth, and faster response with permissible overshoot, undershoot, settling time, and rise time for the IVC IM drive system, compared to prior works.