报告详情
Automatically Tuning of Weighting Factors for FCS-MPC in PMSM Drives Using Lightweight Neural Network
编号：73
访问权限：公开
更新：2023-06-12 15:35:19
浏览：641次
张贴报告
摘要
The deep neural network (DNN) has recently emerged as a compelling approach for predicting the weighting factors of finite control-set model predictive control (FCS-MPC). Nonetheless, the considerable size of DNN parameters necessitates additional computational resources. To address this concern, this paper proposes a lightweight network (L-DNN) strategy to reduce the number of parameters and computational demands for DNN. The proposed method utilizes TensorRT for tensor decomposition and quantization for the trained DNN. As a result, the proposed method occupies fewer logic resources in FCS-MPC, providing the desired behavior with fast dynamic response. Comparative simulations demonstrate that the designed lightweight strategy significantly reduces DNN parameter size without compromising the predictive accuracy. Finally, the dynamic adjustment of weighting factors is validated through simulations on the permanent magnet synchronous motor drives fed by a three-level neutral-point-clamped inverter. More demonstrations and experimental validations will be presented in the full paper.
关键词
Deep neural network, Lightweight network design, Model predictive control, TensorRT, Weighting factors.
报告人
Chunxing Yao
Shuai Xu
Southwest Jiaotong University Zhenyao Sun
Guanzhou Ren
Guohua Li
Guangtong Ma
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重要日期
-
会议日期
06月16日
2023
至06月19日
2023
-
06月15日 2023
报告提交截止日期
-
07月02日 2023
注册截止日期
主办单位
Huazhong University of Science and Technology, China
(IEEE PELS)
IEEE
(IEEE PELS)
IEEE
联系方式
- Organizing Committee
- pr******@hust.edu.cn
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