北京航空航天大学学报 ›› 2007, Vol. 33 ›› Issue (11): 1295-1298.

• 论文 • 上一篇    下一篇

复合舵机的神经网络自校正控制

温肇东1, 王占林1, 祁晓野1, 鲍莉娜2   

  1. 1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100083;
    2. 中国空间技术研究院总体部, 北京 100086
  • 收稿日期:2006-11-28 出版日期:2007-11-30 发布日期:2010-09-17
  • 作者简介:温肇东(1981-),男,辽宁沈阳人,博士生,wenzhaodong@asee.buaa.edu.cn.
  • 基金资助:

    国防空军预研项目(102010501)

Artificial-neural-network adaptive control of compound actuator

Wen Zhaodong1, Wang Zhanlin1, Qi Xiaoye1, Bao Lina2   

  1. 1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. Department of Integration Engineering, China Academy of Space Technology, Beijing 100086, China
  • Received:2006-11-28 Online:2007-11-30 Published:2010-09-17

摘要: 针对复合舵机的时变和非线性特性,提出了神经网络自校正控制方法.用改进的非线性自回归滑动平均模型(NARMA)对复合舵机进行动态建模,采用多层感知器神经网络辨识舵机非线性模型,由广义逆思想设计出控制器,并根据被控对象与辨识模型间误差在线调整网络权值,进而修正控制律,实现了复合舵机的自校正控制.仿真结果表明,在模型有时变及非线性因素的情况下,此方法仍能得到较好的控制效果.

Abstract: To solve time-varying and nonlinear problem of the compound actuator ,the neural-network adaptive control principle was proposed. Establish the dynamic model of compound actuator using nonlinear auto-regressive moving average model, multi-layer perception neural network was used to identify the nonlinear model. The controller was based on the theory of generalized inverse and the network weights were deduced with respect to the error between the object and the identified model. The self-turning control was realized in combination with control-algorithm modify online. The simulation results show that this control scheme has good effect on the control of compound actuator under the model having time-varying and nonlinear character.

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