Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employe...Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.展开更多
针对实际应用中基于动态工况下电池状态参数的片段数据进行电池健康状态(state of health,SOH)实时估计的问题,提出基于动态工况下锂离子电池状态参数(电压、电流、温度)实测数据二维特征图像和深度学习的锂离子电池容量估计算法。首先...针对实际应用中基于动态工况下电池状态参数的片段数据进行电池健康状态(state of health,SOH)实时估计的问题,提出基于动态工况下锂离子电池状态参数(电压、电流、温度)实测数据二维特征图像和深度学习的锂离子电池容量估计算法。首先,将动态工况下电池状态参数监测量(电压、电流和温度)的片段数据转化为二维特征图像。其次,提出基于残差卷积神经网络(residual convolutional neural network,Res-CNN)和门控循环单元(gate recurrent unit,GRU)网络结合的多通道深度学习模型Res-CNN-GRU,以构建动态工况下电池状态参数特征图像和SOH之间的复杂非线性关系,其中电压、电流和温度的二维特征图像以三通道的方式输入到Res-CNN-GRU模型中,模型输出为对应电池的相邻参考充放电循环实验所获得容量的差值。研究结果表明:此方法在锂电池随机充放电工况下对电池健康状态估计效果更佳,且Res-CNN-GRU模型的泛化性和全局特征提取能力较强。论文研究为现实工况下电池健康状态估计的进一步深入研究提供了参考。展开更多
基金Project(60634020) supported by the National Natural Science Foundation of China
文摘Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.
