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The magnitude of current harmonics depends on the design of an electrical machine. By suppressing these harmonics noise can be reduced and efficiency improved. Iterative Learning Control (ILC) has proven effective in reducing harmonics. One of the challenges of working with ILC is operation at varying speeds. Variable speeds are particularly important for applications like automotive drives. The ILC period length changes during the learning process at varying speeds. Due to fixed sample rates, the number of values processed by the ILC varies with motor speed.This paper proposes a method to solve this problem and uses ILC at varying speeds. The ILC used to eliminate the harmonics is based on the inverse system. The usage of a two-dimensional memory array is proposed. This data structure holds rows for specific speeds between which interpolation is performed, enabling the elimination of errors which are periodically cyclic to one electrical rotation. This includes the reduction of the motor current harmonics. To verify the presented method a permanent magnet synchronous motor with distinctive 5th and 7th harmonics is used. In real-time implementations, limitations of memory and computational capacity occur.
Electrical machines generate unwanted flux and current harmonics. Harmonics can be suppressed using various methods. In this paper, the harmonics are significantly reduced using Iterative Learning Control (ILC) and Neural Networks (NNs). The ILC can compensate for the harmonics well for operation at constant speed and current reference values. The NNs are trained with the data from the ILC and help to suppress the harmonics well even in transient operation. The simulation model is based on flux and torque maps, depending on dq-currents and the electrical angle. The maps are generated from FEM simulation of an interior permanent magnet synchronous machine (IPM) and are published with the paper. They are intended to serve other researchers for direct comparison with their own methods. Simulation results in this paper verify that by using ILC and NNs together, current harmonics in transient operation can be eliminated better than without NNs.
Electrical machines exhibit more or less strong current harmonics depending on their physical structure. The more slots are used per strand, the fewer harmonics an electrical machine has [1]. However, the manufacturing costs also increase with the number of slots. In permanent magnet synchronous machines (PMSM), the arrangement of the permanent magnets also influences the amplitude of the harmonics. Current harmonics can lead to increased motor noise and higher power losses. Therefore, it is worthwhile to deal with special controls which suppress these current harmonics. The aim of this work is to find a suitable control strategy for the suppression of current harmonics and to find out which method is suitable for which application. For this purpose, this paper presents and compares three types of control algorithms.
Electrical machines generate unwanted flux and current harmonics. Harmonics can be suppressed by using various methods. In this paper, the harmonics are reduced by using iterative learning control (ILC) and neural networks (NNs). This paper focuses on the startup behavior of the control system. The ILC can compensate well for the harmonics in operation at constant speed and constant current reference values, but needs multiple rotations to learn. The NNs are trained with the data from the ILC and help to suppress the harmonics well even in transient operation and from the first rotation. The simulation model is based on flux and torque maps, depending on dq-currents and the electrical angle. The methods are also applied on the test bench and measurement results are presented.
