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Tension identification of two-motor system based on neural network left-inverse

机译:基于神经网络左逆的两电机系统张力识别

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Tension detection is a key to improve performance of two-motor system under sensorless operation. This paper presents a new identification method for two-motor system based on artificial neural network and the left-inverse theory. Considering that the system parameters are time-variant and the mathematic model of left-inverse identification is complex, BP neural network is used to build the left-inverse model in this method, which is easy to implement. A simulation model of a two-motor system is developed. The simulated results verify the proposed method. By using this control strategy, the tension can be identified quickly and accurately, in which satisfactory robustness is offered.
机译:张力检测是在无传感器操作下提高两电机系统性能的关键。提出了一种基于人工神经网络和左逆理论的双电机系统辨识方法。考虑到系统参数是随时间变化的,且左逆识别的数学模型比较复杂,该方法采用BP神经网络建立左逆模型,易于实现。建立了两电机系统的仿真模型。仿真结果验证了该方法的有效性。通过使用这种控制策略,可以快速,准确地识别张力,从而提供令人满意的鲁棒性。

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