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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 PMmachine are presented for verification of the
proposed control schemes.