Fault Detection and Accommodation for Nonlinear Systems Using Fuzzy Neural Networks
Fault Detection and Accommodation for Nonlinear Systems Using Fuzzy Neural Networks
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DOI：
10.1109/ipemc.2006.4778342
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发表时间：
2006-08
期刊：
影响因子：
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通讯作者：
H. Xue;J.G. Jiang
中科院分区：
文献类型：
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作者：
H. Xue;J.G. Jiang
提出了一种基于模糊神经网络的非线性系统故障检测与调节方法。设计故障参数检测故障，引入自适应更新方法估计和跟踪故障，利用模糊神经网络调整故障参数，构造自动故障诊断，利用故障估计给出的故障补偿控制力实现故障调节。该框架结构简单，检测准确。在无刷直流电机上的仿真结果表明，在电机参数变化故障和负载转矩扰动的情况下，该系统仍能正常工作，具有较高的动态性能和控制精度
A fault detection and accommodation method based on fuzzy neural networks was presented for nonlinear systems. The fault parameters was designed to detect the fault, adaptive updating method was introduced to estimate and tracking fault, fuzzy neural networks was used to adjust the fault parameters and construct automated fault diagnosis, and the fault compensation control force, which given by fault estimation, was used to realize fault accommodation. This framework leaded to a simple structure and an accurate detection. The simulation results in brushless DC motor showed that it was still able to work well with high dynamic performance and control precision under the condition of motor parameters' variation fault and load torque disturbance