关键词: 人工神经网络, 自整定, 电动机, Brandt-Lin算法

Abstract: With the development of electric power and electron technique，microelectronic technique and new electromotor control theory，BLDCM spreads rapidly.Not only can it possess the dynamic and static speed regulating performances as the same as the direct current electromotor，but also have the advantages such as avoiding the limitation of electrical brush structure，a smaller capacity，a higher efficiency and easy to control.The speediness，stability and robustness of a BLDCM direct current speed regulation system are the important guidelines in evaluating the performance of an electromotor.This paper introduces a method that uses the neural networks control mode in a faster and more accurate BLDCM control system to increase the dynamic response and robustness.After the application of neural networks self-adapting control algorithm，there will be no manually operation in parameters setting.The control parameters can be recognized and set automatically according to the work condition changes of the system and as a result，the control precision is increased significantly，the regulating time is reduced and the control process has a better control performance.The neural networks self-adapting control algorithm in this paper is Brandt-Lin algorithm and the activation functions as well as learning speed are improved so that the control speed and precision are increased.A nonlinear function is also added in this algorithm that can increase the adaptability of the neural network in higher-order system.

Key words: neural networks, self-tuning, BLDCM, Brandt-Lin algorithm