| 引用本文: | 解旭辉,李圣怡,C. Jimes Li.基于神经网络的超精密机床伺服进给非线性模型辨识.[J].国防科技大学学报,1997,19(3):75-79.[点击复制] |
| Xie Xuhui,Li shengyi,C. JimesLi.Non-linear Model Identification By Neural Network for Servo-feed System of the Ultra-precision Machine Tools[J].Journal of National University of Defense Technology,1997,19(3):75-79[点击复制] |
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| 基于神经网络的超精密机床伺服进给非线性模型辨识 |
| 解旭辉1, 李圣怡1, C. Jimes Li2 |
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(1.国防科技大学 机械电子工程与仪器系 湖南 长沙 410073;2.Dept ME, AE&M, Renselaer Institute of Technology, Troy City N.Y.,USA)
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| 摘要: |
| 以双轴T型结构的超精密金刚石车床的伺服进给系统为研究对象, 采用自构造神经网络技术, 建立了系统的非线性动态数学模型, 为系统非线性控制与补偿提供参考模型。 |
| 关键词: 神经网络, 自构造学习算法, 伺服进给系统, 超精密机床 |
| DOI： |
| 投稿日期：1996-10-04 |
| 基金项目: |
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| Non-linear Model Identification By Neural Network for Servo-feed System of the Ultra-precision Machine Tools |
| Xie Xuhui1, Li shengyi1, C. JimesLi2 |
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(1.Department of Mechantronics Engineering and Instrumentation, DUDT, Changsha, 410073;2.Dept ME. AE&M, Renselaer Institute of Technology, Troy City N.Y.,USA)
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| Abstract: |
| A new forword NN structural and weight learning algorithm is adopted to build the non-linear dynamical NN models for the servo-feed systems of a“T”type ultra-precision machine tools. Based on these NN models, an adaptive MRAC can be devoloped to control and compensate its non-linearities. |
| Keywords: neural-network, structual & weight learning algorithm, servo-feed system, ultra-precision machine tools |
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