Nested-loop neural network vector control of permanent magnet synchronous motors

Abstract

With the improvement of battery technology over the past two decades and automotive technology advances, more and more vehicle manufacturers have joined in the race to produce new generation of affordable, high-performance Electric Drive Vehicles (EDVs). Permanent Magnet Synchronous Motors (PMSMs) are at the top of AC motors in high performance drive systems for EDVs. Traditionally, a PMSM is controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show serious limitations. This paper investigates how to mitigate such problems using a nested-loop neural network architecture to control a PMSM. The neural network implements a dynamic programming algorithm and is trained using backpropagation through time. The performance of the neural controller is studied for typical vector control conditions and compared with conventional vector control methods, which demonstrates the neural vector control strategy proposed in this paper is effective. Even in a highly dynamic switching environment, the neural vector controller shows strong ability to track rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for complex EDV drive needs.