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Adaptive speed observer using artificial neuralnetwork for sensorless vector control of inductionmotor drives

机译：使用人工神经网络的自适应速度观测器，用于感应电动机驱动器的无传感器矢量控制

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摘要

This paper presents an adaptive speed observer for an induction motor using a neural artificial network with a direct field-oriented control drive. The Speed and rotor flux are estimated with the only assumption that from stator voltages and currents are measurable. The estimation algorithm uses a state observer combined with an intelligent adaptive mechanism based on a recurrent neural network (RNN) to estimate rotor speed. The stator and rotor resistances are estimated by a simple Proportional-Integrator (PI) controller, which reduces sensitivity to variations, due essentially to the influence of temperature. The proposed sensorless control scheme is tested for various operating conditions of the induction motor drive. The proposed sensorless control scheme is tested for various operating conditions of the induction motor drive. Experimental results demonstrate a good robustness against load torque disturbances, the estimated fluxes and rotor speed converge to their true values, which guarantees that a precise trajectory tracking with the prescribed dynamics.
机译：本文提出了一种感应电动机的自适应速度观测器，该观测器使用带有直接磁场定向控制驱动器的神经人工网络。仅基于从定子电压和电流可测量的假设来估算速度和转子磁通。估计算法使用状态观察器和基于递归神经网络（RNN）的智能自适应机制相结合来估计转子速度。定子和转子的电阻由简单的比例积分器（PI）控制器估算，该控制器降低了对变化的敏感性，这主要是由于温度的影响。针对感应电动机驱动器的各种运行条件，对提出的无传感器控制方案进行了测试。针对感应电动机驱动器的各种运行条件，对提出的无传感器控制方案进行了测试。实验结果表明，它对负载转矩扰动具有良好的鲁棒性，所估算的磁通和转子速度收敛于其真实值，从而保证了具有规定动力学特性的精确轨迹跟踪。

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