Fault-Tolerant Operation of Multiphase Permanent-Magnet Machines Using Iterative Learning Control

Abstract

Fault-tolerant control (FTC) techniques for multiphase permanent magnet (PM) motors are usually designed to achieve maximum ripple-free torque under fault conditions with minimum ohmic losses. A widely accepted approach is based on flux distribution or back EMF (BEM) model of the machine to calculate healthy phase currents. This is essentially an open-loop technique where currents are determined (based on motor fault models) for each fault scenario. Therefore, it is highly model dependent. Since torque pulsation due to open-circuit faults and short-circuit faults are periodic, learning and repetitive control algorithms are excellent choices to minimize torque ripple. In this paper, iterative learning control (ILC) is applied as a current control technique for recovering performance in multiphase PM motor drives under fault conditions. The ILC-based FTC needs torque measurement or estimation, but avoids the need for complicated fault detection and fault diagnosis algorithms. Furthermore, BEM-based FTC and ILC-based FTC are proposed that initiates the learning from a model-based approximate guess (from the BEM method). Therefore, this method combines the advantages of both model information as well as robustness to model uncertainty through learning. Hence, the proposed method is well suited for high-performance safety critical applications. Finite element analysis and experimental results on a five-phase PM machine are presented for verification of the proposed control schemes.