Abstract
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). 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). 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 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). 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. 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.
Highlights
Stringent emission laws and regulations and increased demand for fuel-efficient, high-performance vehicles are compelling the automobile manufacturers worldwide to equip their vehicles with alternative fuel technologies, which results in increased overall market adoption of electric vehicles (EVs).The global EV market was valued at $103,342 million in 2016 and it is projected to reach $350,963 million by 2023, growing at a compound annual growth rate (CAGR) of 19.8% from 2017 to 2023 [1].The key EV manufacturers, such as Toyota Motor Corp., Volkswagen AG, and Tesla, expanded their business by globally opening various research and manufacturing facilities
interior mounted permanent magnet (IPM) motors are widely used in electric drive applications, in electric drive vehicles and drones
This paper presents a digital signal processor (DSP) implementation of approximate dynamic programming (ADP)-based optimal control that is based on artificial neural networks for vector control of IPM motors operating in linear and over-modulation modes
Summary
Stringent emission laws and regulations and increased demand for fuel-efficient, high-performance vehicles are compelling the automobile manufacturers worldwide to equip their vehicles with alternative fuel technologies, which results in increased overall market adoption of electric vehicles (EVs). The key EV manufacturers, such as Toyota Motor Corp., Volkswagen AG, and Tesla, expanded their business by globally opening various research and manufacturing facilities. They have launched more efficient and advanced EVs to enhance their market share. 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 most widely used electric motor in the automobile industry is permanent magnet synchronous motors (PMSM). There are two main types of permanent magnet (PM) motors: surface mounted PM (SPM)
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