| 磁悬浮改进RBF神经网络控制的仿真研究 |
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| 引用本文： | 张静,裴雪红,邢海峰.磁悬浮改进RBF神经网络控制的仿真研究[J].哈尔滨理工大学学报,2011,16(1):48-52. |
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| 作者姓名： | 张静 裴雪红 邢海峰 |
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| 作者单位： | 1. 哈尔滨理工大学自动化学院,黑龙江,哈尔滨,150080
2. 中国石油天然气管道通信电力工程总公司,河北,廊坊,065000 |
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| 基金项目： | 黑龙江省自然科学基金，黑龙江省教育厅科学技术研究项目 |
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| 摘 要： | 利用RBF神经网络的自学习、自适应能力,在传统PID控制基础上,提出一种改进的RBF在线辨识的自适应PID控制方法.为避免系统启停过程中,短时大偏差引起的超调较大,文中利用RBF网络提供辨识信息实现对参数KP、KD调整,对参数KI不做整定,以满足磁悬浮系统的动态和静态性能要求.设计中采用S-函数建立磁悬浮系统的非线性模...
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| 关 键 词： | 磁悬浮 RBF网络 自适应 PID控制 |
Improved RBF Neural Network Control of Magnetic Levitation |
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| ZHANG Jing,PEI Xue-hong,XING Hai-feng.Improved RBF Neural Network Control of Magnetic Levitation[J].Journal of Harbin University of Science and Technology,2011,16(1):48-52. |
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| Authors: | ZHANG Jing PEI Xue-hong XING Hai-feng |
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| Affiliation: | ZHANG Jing1,PEI Xue-hong1,XING Hai-feng2(1.College of Automation,Harbin University of Science and Technology,Harbin 150080,China,2.China Petroleum and Gas Pipeline Telecom and Electricity Engineering Corporation,Langfang 065000,China) |
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| Abstract: | As magnetic levitation system has the characteristics of nonlinearity and open-loop instability,it is difficult to achieve ideal effect with conventional control.In this paper,an improved RBF on-line identification method of adaptive PID control is proposed based on traditional PID control and the self-learning,adaptive capacity of RBF neural networks.In order to satisfy the static and dynamic performance requirements of magnetic levitation system and avoid the large overshoot following the short and large ... |
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| Keywords: | magnetic levitation radial basis function network adaptive PID control |
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