Fuzzy Predictive DTC of Induction Machines with reduced Torque Ripple and High Performance Operation

This paper presents an enhanced strategy for Direct Torque Control (DTC) combining artificial intelligent (AT) and predictive algorithms. The advantages of both methodologies are merged to solve the main problems of closed loop controlled induction machines (IM) and, in particular the drawbacks of the classical DTC. Predictive DTC (P-DTC) methods solve the problems of the high torque ripple and poor performance at both starting condition and low mechanical speed operation. However these strategies depend on the IM parameter's knowledge. A new approach of fuzzy logic control (FLC) with dynamic rules based on the laws of predictive DTC is proposed to reduce the parameter dependency and improve the performance of the P-DPC. The predictive rule's main idea is to compute the angle difference in between the lines of constant torque and constant stator flux magnitude expressed as a function of the (alpha beta) inverter voltage components. For verification purposes, simulations of the DTC, P-DTC and proposed Fuzzy Predictive DTC (FP-DTC) were conducted and compared. Experimental results for the three controllers confirm the expected performance of the proposed algorithm.