Model Correction Control Strategy for Direct Drive Permanent Magnet Synchronous Motor Servo System
文献类型：会议论文
| 作者 | Huang LS(黄梁松); Qu DK(曲道奎); Xu F(徐方) |
| 出版日期 | 2010 |
| 会议名称 | 22nd Chinese Control and Decision Conference |
| 会议日期 | May 26-28, 2010 |
| 会议地点 | Xuzhou, China |
| 关键词 | direct drive model correction Kalman filter and state observer |
| 页码 | 1467-1470 |
| 中文摘要 | Although it has many advantages such as high mechanical rigidity, fast dynamic response and high efficiency, a direct drive permanent magnet synchronous servo motor is sensible to non-linearity, variety of parameter and external disturbance. This paper presents a model correction control strategy, which corrects the unmodeled dynamics of control system and suppresses disturbances by a unique control structure with a state observer based on a Kalman filter and a digital low-pass filter, Theoretical and experimental results indicate that this strategy can effectively eliminate high order dynamic, nonlinearity, measurement noise and variation of load torque in the control system, and improves a direct drive servo control system's stability, robustness and resistance to disturbances. |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 1 |
| 会议主办者 | NE Univ China, IEEE Ind Elect Chapter, China Univ Mining & Technol, IEEE Control Syst Soc, IEEE Ind Elect Soc, Chinese Assoc Aeronautics, Automatic Control Soc, Chinese Assoc Automat, Appl Soc, Chinese Assoc Syst Simulat, Simulat Methods & Model Soc, Chinese Assoc Artificial Intelligence, Intelligent Control & Management Soc |
| 会议录 | 2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | Piscataway, NJ, USA |
| 语种 | 英语 |
| ISBN号 | 978-1-4244-5182-1 |
| WOS记录号 | WOS:000290460300295 |
| 源URL | [http://ir.sia.cn/handle/173321/7666]  |
| 专题 | 沈阳自动化研究所_其他 |
| 推荐引用方式 GB/T 7714 | Huang LS,Qu DK,Xu F. Model Correction Control Strategy for Direct Drive Permanent Magnet Synchronous Motor Servo System[C]. 见:22nd Chinese Control and Decision Conference. Xuzhou, China. May 26-28, 2010. |
入库方式： OAI收割
来源：沈阳自动化研究所
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