基于神经网络的磁编码器补偿算法研究

磁电编码器在使用过程中，由于霍尔传感器本身的温度特性，外界环境温度变化会导致霍尔传感器的输出值随之改变，进而使磁电编码器的输出值发生变化，导致检测角度误差，研究过程中提出了一种基于神经元的自适应最优速算法，建立温漂模型，构造误差函数，通过有限次的迭代不断更正温度系数，使磁电编码器的输出值无限接近真实角度值，从而实现角度校正，实验表明，该方法具有显著的效果。

Research on Compensation Algorithm of Magnetic Encoder Based on Neural Network

During the use of the magnetoelectric encoder, due to the temperature characteristics of the Hall sensor itself, changes in the external ambient temperature will cause the output value of the Hall sensor to change accordingly, which will cause the output value of the magnetoelectric encoder to change, resulting in detection angle errors. During the research, a neuron-based adaptive optimal speed algorithm was proposed. A temperature drift model was established, an error function was constructed, and the temperature coefficient was continuously corrected through a limited number of iterations, so that the output value of the magnetoelectric encoder was infinitely close to the true angle. Value to achieve angle correction, experiments show that this method has significant effects.