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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Junyou Yang Lirong Guan Yongcheng Zhao |
| Copyright Year | 2009 |
| Description | Author affiliation: Mathematics Department, ZhengZhou University, Zhengzhou, China (Yongcheng Zhao) || School of Electrical Engineering, Shenyang University of Technology, Shenyang, China (Junyou Yang; Lirong Guan) |
| Abstract | An recurrent fuzzy neural network (RFNN) compensation strategy combined with the backstepping approach for permanent magnet linear synchronous motors (PMLSMs) drive is proposed. Considering the lumped uncertainties with parameter variations and external disturbance for an actual PMLSM drives, a backstepping control law is derived by backstepping design technique. However, the upper bound of the lumped uncertainty is difficult to obtain in advance in practical applications. A RFNN observer is proposed to adapt the value of lumped uncertainty, the online parameters training of the RFNN is derived by the gradient descent method. Simulated results show that the proposed method possesses better position tracking performances and robustness to lumped uncertainty for PMLSM drive. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 528110 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424445462 |
| DOI | 10.1109/TENCON.2009.5395827 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-01-23 |
| Publisher Place | Singapore |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Synchronous motors Uncertainty Backstepping Drives Upper bound Fuzzy neural networks Fuzzy control Control systems Robustness Programmable control recurrent fuzzy neural network(RFNN) permanent magnet linear synchronous motor (PMLSM) gradient descent method backstepping control |
| Content Type | Text |
| Resource Type | Article |
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