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Indirect field oriented control of an induction motor implemented with an artificial neural network.

机译：用人工神经网络实现的感应电动机的间接磁场定向控制。

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摘要

Presented in this dissertation is an artificial neural network that is designed to perform indirect field orientation control for an induction motor speed control system. A multi-layer feedforward network with the hypertangent sigmoid transfer function is chosen as the controller that generates current command signals. The neural network inputs are the induction motor speed, synchronous frame q-axis current and a delayed sample of this current, the stationary frame q and d axis currents are the current control command outputs, which are also fed back to the neural network input after one sample delay. The neural network has two neurons at the output layer, ten neurons in the hidden layer, and five input neurons.; It is shown that due to the recurrent structure of the neural network and the excessive output error of the neural network, off-line training is not sufficient for stable operation of the system. An improved training algorithm employing experimental data is proposed to remedy this problem.; The synchronous frame space vector current regulator method is chosen as the current control approach employed for experimental verification of the controller. In this standard current regulator method the zero voltage sequence is applied equally in the beginning and at the end of each sampling interval which results in low ripple current, but it does not guarantee the least current error during the sampling interval. A modified space vector current regulator is proposed in this dissertation that optimizes the zero voltage time interval which reduces the transient current error in the motor stator and improves the output torque tracking capability.; The experimental setup to test the neural network includes a Texas Instruments TMS320C30 floating-point digital signal processing board operating at 33MHz. Interface circuits are designed to measure the current and speed feedback signals and to control the switching of the DC/AC converter.; The neural network calculations are performed on the digital signal processing board. Programming techniques are presented to increase the speed of the computation. Experimental results indicate successful design and implementation of the neural network. A 1ms sampling interval is employed with a total of 330{dollar}mu s{dollar} required for all neural network, current regulator and system calculations. This leaves 67% of the digital signal processor computation capacity available for other purposes as might be required in a commercial drive system. It is also verified that as expected from the simulation results, two stages of training are required for stable system operation employing the neural network. The main limitation of the controller is shown to be the low-speed region of operation {dollar}(<200rpm){dollar}, where the neural network output error causes large output speed error.
机译：本文提出了一种人工神经网络，其设计用于对感应电动机速度控制系统进行间接的磁场定向控制。选择具有超正切S型传递函数的多层前馈网络作为生成当前命令信号的控制器。神经网络输入是感应电动机速度，同步框架q轴电流和该电流的延迟采样，静止框架q和d轴电流是电流控制命令输出，在控制后也将反馈给神经网络输入一采样延迟。神经网络在输出层有两个神经元，在隐藏层有十个神经元，还有五个输入神经元。结果表明，由于神经网络的递归结构和神经网络的输出误差过大，离线训练不足以使系统稳定运行。提出了一种改进的利用实验数据的训练算法来解决这个问题。选择同步帧空间矢量电流调节器方法作为用于控制器实验验证的电流控制方法。在这种标准的电流调节器方法中，在每个采样间隔的开始和结束时均会施加零电压序列，这会导致纹波电流较低，但不能保证在采样间隔内的电流误差最小。本文提出了一种改进的空间矢量电流调节器，可以优化零电压时间间隔，从而减小电机定子的瞬态电流误差，提高输出转矩的跟踪能力。用于测试神经网络的实验装置包括运行在33MHz的Texas Instruments TMS320C30浮点数字信号处理板。接口电路设计用于测量电流和速度反馈信号，并控制DC / AC转换器的开关。神经网络计算在数字信号处理板上执行。提出了编程技术以提高计算速度。实验结果表明神经网络的成功设计和实现。所有神经网络，电流调节器和系统计算都采用1ms的采样间隔，总共需要330 {μs} s {dollar}。这使得数字信号处理器的67％的计算能力可用于商业驱动系统中可能需要的其他用途。还证实了，正如从仿真结果所期望的那样，使用神经网络进行的稳定系统操作需要两个阶段的训练。控制器的主要局限性显示为低速运行区域{dolal}（<200rpm）{dollar}，其中神经网络输出误差会导致较大的输出速度误差。

著录项

  • 作者

    Mohamadian, Mustafa.;

  • 作者单位

    University of Calgary (Canada).;

  • 授予单位 University of Calgary (Canada).;
  • 学科 Engineering Electronics and Electrical.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 1998
  • 页码 156 p.
  • 总页数 156
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;人工智能理论;
  • 关键词

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