Abstract
An interior permanent magnet synchronous motor (IPMSM) drive system with machine learning-based maximum torque per ampere (MTPA) as well as flux-weakening (FW) control was developed and is presented in this study. Since the control performance of IPMSM varies significantly due to the temperature variation and magnetic saturation, a machine learning-based MTPA control using a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) was developed. First, the d-axis current command, which can achieve the MTPA control of the IPMSM, is derived. Then, the difference value of the dq-axis inductance of the IPMSM is obtained by the PPFNN-AMF and substituted into the d-axis current command of the MTPA to alleviate the saturation effect in the constant torque region. Moreover, a voltage control loop, which can limit the inverter output voltage to the maximum output voltage of the inverter at high-speed, is designed for the FW control in the constant power region. In addition, an adaptive complementary sliding mode (ACSM) speed controller is developed to improve the transient response of the speed control. Finally, some experimental results are given to demonstrate the validity of the proposed high-performance control strategies.
Highlights
interior permanent magnet synchronous motor (IPMSM) have many attractive characteristics, including wide speed operating range, high-power density, and high torque-to-inertia ratio
IPMSMs has been utilized in many industrial applications [1,2,3,4]
The control characteristic of IPMSMs tends to time-varying behavior due to the machine parameters variation caused by the magnetic saturation
Summary
IPMSMs have many attractive characteristics, including wide speed operating range, high-power density, and high torque-to-inertia ratio. The control characteristic of IPMSMs tends to time-varying behavior due to the machine parameters variation caused by the magnetic saturation. For the IPMSM, the quadrature-axis inductance is increased by the rotor magnetic circuit saliency, which leads to a reluctance torque term incorporating into the torque equation [5]. To utilize the advantage of the reluctance torque term in the constant torque and constant power region, appropriate control methods are required. An MTPA control has been proposed to improve the torque output in the constant torque region [5,6,7,8,9,10]. In [8], the method of using the high-frequency variation of the output mechanical power combined with a fuzzy-logic controller to obtain the advance angle of the MTPA for an IPMSM was proposed
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