Function method g measurement of the hottest brush

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Brushless DC motor function method g measurement

in order to improve 4.3 pigments and additives should comply with the provisions of relevant standards, in fact, it will not have a harmful impact on product performance. Brushless DC motor sensorless control accuracy, a new speed independent control strategy of Brushless DC motor based on RBF neural network is proposed. The strategy mainly includes three parts: on the one hand, using the excellent performance of RBF neural network, such as adaptive and nonlinear control, combined with the motor running state, repair the connection weight of the neural network, so as to overcome the negative impact of accuracy decline caused by the nonlinear interference of Brushless DC motor system and the uncertainty of some parameters. On the other hand, after the output of neural network is filtered, the speed independent position function method (function method) is used to output the motor commutation signal. This method can measure the rotor position and give the commutation time when the rotor speed changes from near zero to high speed. The simulation results show that the strategy has excellent control performance

brushless DC motor has many remarkable advantages, such as simple structure, flexible control, reliable operation and so on. It has been widely used in the industrial field. However, the sensor installed inside the motor to detect the rotor position is easy to introduce electromagnetic interference, which limits the miniaturization of the motor and its application in the bad environment

sensorless control technology does not rely on position sensors. It uses the phase voltage and phase current of the motor to observe the rotor position and analyze the commutation time, which improves the reliability, anti-interference ability and environmental adaptability of the system. The commonly used control methods without position sensor mainly include the following: 1. The EMF Detection Method of checking the reliable grounding of electrical appliances with bare copper wires is simple and reliable, but it is not applicable at low speed and when the rotor is stationary; The state observer method was proposed earlier, but it is only applicable to brushless DC motors with sinusoidal induction electromotive force; The stator inductance method improves the low-speed performance of the back EMF method, but it requires real-time temporary measurement of the winding inductance, which is difficult to realize. Function method g, also known as speed independent position function method, can detect the rotor position and give the commutation time when the rotor speed is close to zero to high speed. However, due to the nonlinearity of the motor and the uncertainty of some system parameters, the accuracy of the fine grinding cylinder will be reduced

rbf neural network is a three-layer forward network, which was proposed by j.moody and c.darken in the late 1980s. It has strong self-learning and adaptive ability, and can simulate the nonlinear mapping relationship between any input and output. In this paper, RBF neural network combined with G function method is used to estimate the rotor commutation time. According to the motor running state, the connection weight of neural network is modified to obtain the ideal commutation signal. Using the nonlinear mapping ability of neural network and the advantage that G function method is independent of speed, the control accuracy of the system is improved. Simulation and experimental results show that this method can accurately detect the commutation signal of the rotor and has excellent control performance

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