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Current Threshold On-line Identification Control Theme based on Intelligent Controller for Four-switch Three-phase Brushless DC Motor

机译:基于四开关三相无刷直流电动机智能控制器的电流阈值在线识别控制主题

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摘要

The brushless DC motor has such advantages as simple structure, convenient to control, high reliability, and has been applied in many industrial fields. In order to simplify the converter topology and lower the system cost, four-switch three-phase BLDCM recently becomes research highlight of scholars. Conventional hysteresis controllers suffer from big phase current ripple and inaccuracy current threshold adjusting of the four-switch three-phase BLDCM. To overcome the shortcomings of the hysteresis controller, this paper presents a novel direct current control strategy based on current threshold on-line identification using intelligent controller for four-switch three-phase BLDCM. A radial basis function neural network is built to identify the relationship of load, current threshold and expected speed on-line. When the given speed and load is setting, current threshold identifier give the suitable threshold output to the current controller. Also the system use two PID controller based on RBF neural network on-line regulation to control phase current I{sub}a and I{sub}b separately. Current controller constructs the on-line reference model, implements self-learning of PID controller parameters by RBF neural network. The intelligent controller individually regulate duty cycle of PWM signals working on the inverter bridge to make phase current fall in the specified threshold quickly and smoothly. Simulated and experimental systems are build to fully prove the performance of the control scheme. Excellent flexibility and adaptability as well as high precision and good robustness are obtained by the proposed strategy.
机译:无刷直流电动机具有结构简单,可方便的控制,高可靠性,并应用于许多工业领域。为了简化转换器拓扑和降低系统成本,四个开关三相BLDCM最近成为学者的研究亮点。传统的滞后控制器遭受大相电流纹波和四相三相BLDCM的不准确电流阈值调整。为了克服滞后控制器的缺点,本文基于使用智能控制器的电流阈值在线识别为四开关三相BLDCM,提出了一种新的直流控制策略。建立径向基函数神经网络以识别负载,电流阈值和预期速度的关系。当给定的速度和负载是设定时,电流阈值标识符使得对电流控制器的合适阈值输出。此外,系统使用基于RBF神经网络的PID控制器在线调节,以单独控制相电流I {SUB} A和I {SUB} B。电流控制器构造在线参考模型,通过RBF神经网络实现PID控制器参数的自学。智能控制器可单独调节在逆变器桥上工作的PWM信号的占空比,以便快速且平滑地在指定的阈值下落下相电流。模拟和实验系统是建立的,以完全证明控制方案的性能。通过所提出的策略获得了优异的灵活性和适应性以及高精度和良好的稳健性。

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