To improve the efficiency of a permanent magnet (PM) motor, it is usually required to extend the motor operation from linear to over or even six-step modulation regions based upon the space vector pulsewidth modulation technique. Traditionally, a PM motor is controlled by using the standard vector control technique. However, a recent study shows the decoupling inaccuracy of designing PM motor control using the conventional standard vector control approach. This issue has caused a challenge to control a PM motor, particularly when the motor operates from linear to overmodulation regions. In this article, a novel approximate dynamic programming (ADP) vector controller is developed to overcome the challenge. The ADP controller is developed using the full dynamic equation of a PM motor and implemented using an artificial neural network (ANN). A feedforward control strategy is integrated with the -based ADP controller to enhance the performance of the controller in both linear and overmodulation regions. The ANN-based ADP control is compared with the conventional standard vector control and a model predictive control. Simulation and hardware experiments demonstrate that the proposed controller can track reference changes better with high efficiency and reliability for PM motor operation in linear and overmodulation regions.

中文翻译：

---

用于 IPM 电机在线性和过调制区域运行的近似动态规划矢量控制器


为了提高永磁（PM）电机的效率，通常需要基于空间矢量脉宽调制技术将电机运行从线性扩展到超甚至六步调制区域。传统上，永磁电机是通过使用标准矢量控制技术来控制的。然而，最近的一项研究表明，使用传统的标准矢量控制方法设计永磁电机控制时存在解耦的不准确性。这个问题给永磁电机的控制带来了挑战，特别是当电机从线性区域运行到过调制区域时。在本文中，开发了一种新颖的近似动态规划（ADP）矢量控制器来克服这一挑战。 ADP 控制器是使用 PM 电机的完整动态方程开发的，并使用人工神经网络 (ANN) 实现。前馈控制策略与基于 的 ADP 控制器集成，以增强控制器在线性和过调制区域的性能。基于 ANN 的 ADP 控制与传统的标准矢量控制和模型预测控制进行了比较。仿真和硬件实验表明，所提出的控制器能够更好地跟踪参考变化，并且在线性和过调制区域中实现永磁电机运行的高效率和可靠性。