Fuzzy Predictive DTC of Induction Machines With Reduced Torque Ripple and High-Performance Operation

This paper proposes an enhanced strategy for direct torque control (DTC) combining artificial intelligent (AI) and predictive algorithms. The advantages of this merge are in the solution of closed-loop controlled induction machine (IM) problems. Predictive DTC (P-DTC) methods reduce the high torque ripple and improve the performance at both starting condition and low mechanical speed operation. However, P-DTC depends on the IM parameter's knowledge. The approach here is the introduction of fuzzy logic control with dynamic rules based on the P-DTC law's to reduce the parameter dependency and improve the performance of P-DPC. Additional comparative performance study of eight modulation strategies under the proposed fuzzy-predictive DTC (FP-DTC) is conducted. It results that the space-vector modulation (SVM) is the most suitable scheme with the best combination of criteria such stator current total harmonic distortion, switching losses and dynamic behavior. The parameter dependency of the FP-DTC is tested by a sensitivity analysis which corroborates the robustness of the proposed control. For verification purposes, simulations of the DTC, P-DTC, and FP-DTC were conducted and compared. Experimental results for the three controllers and two modulations (pulse width modulation and SVM) confirm the expected performance of the proposed control algorithm and modulation assessment study.