To import advanced intelligent control antilogy into conventional direct torque, then join intelligence optimistic algorithm into conventional neural network control organically, through the network observer built to carry out online observation of electric machine parameter. 在传统的直接转矩中引入先进的智能控制思想,将智能优化算法与传统的神经网络控制有机地结合在一起,通过所构造出的网络观测器来实现对电机参数的在线观测。
Introduced the thesis of the neural network observer. Presents the nonlinear mapping relation between the input and output vectors, and the sensor fault diagnoses method realized by the neural network observer group. 介绍了神经网络观测器原理,给出神经网络观测器输入、输出向量之间的非线性映射关系,以及采用神经网络观测器组实现传感器故障诊断的方法。
Simulation of Sensor Fault Diagnoses Based on the Neural Network Observer Group 基于神经网络观测器组的传感故障诊断仿真
Furthermore, the Lyapunov function is created, and on-line learning rules are given for the network weight matrix, such that the stability of observer is proved. 利用Lyapunov理论设计了网络权系数阵的在线学习规则,并证明了观测器的稳定性。
And then, the delay sampling value of the networked control systems is predicted by neural network. The error equation of the fault observer is constructed based on a neural network prediction, and the stability condition of the observer is proved. 其次,通过对网络控制系统时延采样值进行神经网络预测,建立了基于神经网络预测的故障观测器误差方程,并证明了该观测器稳定的条件。