For an IPM motor, the magnets are buried inside the rotor. For an SPM motor, the magnets of the motor are on the surface of the rotor.
There are two main types of permanent magnet (PM) motors: surface mounted PM (SPM) motor and interior mounted PM (IPM) motor. The most widely used electric motor in the automobile industry is permanent magnet synchronous motors (PMSM). An EV uses one or more electric motors for propulsion. An electric motor is one of the most important EV components. However the high performance and efficiency of EV electric and electronic components are critical in the growth of the EV market due to the limited space within an EV. The NN controller has demonstrated a better current and speed tracking performance than the conventional standard vector controller for IPM operation in both the linear and over-modulation regions. The experiment results show that the proposed NN controller is able to operate with a sampling period of 0.1ms, even with limited DSP resources of up to 150 MHz cycle time, which is applicable in practical motor industrial implementations.
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The DSP-based NN controller is able to manage IPM motor control in linear, over, and six-step modulation regions to improve the efficiency of IPM drives and to allow for the full utilization of DC bus voltage with space-vector pulse-width modulation (SVPWM). A DSP-based NN control system is built for an IPM motor drives system, and a high efficient DSP program is developed to implement the NN control algorithm while considering the limited memory and computing capability of the TMS320F28335 DSP. The NN controller is developed based on the complete state-space equation of an IPM motor and it is trained to achieve optimal control according to approximate dynamic programming (ADP). This paper develops a neural network (NN) vector controller for an interior mounted permanent magnet (IPM) motor by using a Texas Instrument TMS320F28335 digital signal processor (DSP).
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