| 引用本文: | 王家军,马国进.基于Petri模糊神经网络磁通观测器的感应电动机无速度传感器控制[J].控制理论与应用,2010,27(9):1195~1200.[点击复制] |
| WANG Jia-jun,MA Guo-jin.Speed sensorless control for induction motor based on flux observer with Petri fuzzy neural networks[J].Control Theory and Technology,2010,27(9):1195~1200.[点击复制] |
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| 基于Petri模糊神经网络磁通观测器的感应电动机无速度传感器控制 |
| Speed sensorless control for induction motor based on flux observer with Petri fuzzy neural networks |
| 摘要点击 1557 全文点击 906 投稿时间：2009-05-13 修订日期：2009-10-26 |
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| DOI编号 10.7641/j.issn.1000-8152.2010.9.CCTA090604 |
| 2010,27(9):1195-1200 |
| 中文关键词 感应电动机 无速度传感器控制 Petri模糊神经网络 磁通观测器 |
| 英文关键词 induction motor speed sensorless control Petri fuzzy neural networks flux observer |
| 基金项目 浙江省自然科学基金资助项目(Y1080222). |
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| 中文摘要 |
| 利用Petri模糊神经网络构造电流观测器, 基于电流观测值构造感应电动机的转子磁通观测器, 根据磁通观测值进行电动机转子速度的计算. 基于一种新颖的感应电动机解耦模型, 设计了感应电动机的滑模反推控制器, 并给出了Petri模糊神经网络的收敛性证明. 通过MATLAB仿真验证了系统设计的有效性. |
| 英文摘要 |
| Pertri fuzzy neural networks(PFNN) are applied to construct the current observer. The flux observer is constructed based on the observed current. The rotor speed is computed according to the observed rotor flux. Slidingmode backstepping controllers are designed based on a new decoupled model of the induction motor. The proof of PFNN convergence is also given. The effectiveness of the control design is validated through MATLAB simulation. |