中国空间科学技术

中国空间科学技术

• • 上一篇    下一篇

基于改进蚁群算法的飞行仿真转台的控制优化

段海滨;王道波;于秀芬;   

  1. 北京航空航天大学,南京航空航天大学,中国科学院空间科学与应用研究中心 北京100083,南京210016,北京100080
  • 发布日期:2007-08-25

Study on the Improved ACA-based Parameters Optimization of NLPID Controller for Flight Simulator

Duan Haibin1 Wang Daobo2 Yu Xiufen3(1 Beijing University of Aeronautics and Astronautics,Beijing 100083)(2 Nanjing University of Aeronautics and Astronautics,Nanjing 210016)(3 Center for Space Science and Applied Research,Chinese Academy of Sciences, Beijing 100080)   

  • Online:2007-08-25
  • Supported by:
    国家自然科学基金(60604009);; 航空科学基金资助项目(2006ZC51039);; 北京航空航天大学“蓝天新秀”基金资助项目

被引次数

14

摘要： 提出了一种用改进蚁群算法优化飞行仿真转台非线性PID控制参数的新策略,借助相遇搜索策略和信息素残留系数的自适应控制思想对基本蚁群算法进行了改进,设计了一种基于改进蚁群算法优化飞行仿真转台非线性PID(NLPID)控制参数的飞行仿真转台系统结构,在对非线性PID控制参数进行优化时采用了时间乘以误差绝对值积分最小性能指标,最后将用改进蚁群算法优化后的控制参数应用于某型高性能飞行仿真转台。实验表明,采用改进蚁群算法优化非线性PID控制参数的飞行仿真转台系统可从带噪声的输入信号中合理地提取出微分信号,并且对噪声具有很强的滤波作用,整个系统响应速度快,并具有较强的鲁棒性。

关键词: 蚁群算法, 信息素, 非线性比例-微分-积分控制器, 参数优化, 飞行仿真转台

Abstract: A novel NLPID optimization strategy for flight simulator was proposed.In order to enhance the global convergence performance of ACA,the basic ACA was improved by using meeting search strategy and trail decay coefficient self-adaptive adjusting ideology.Furthermore,an improved ACA-based NLPID parameters optimization structure for flight simulator was designed.ITAE performance criteria was adopted in the improved ACA.Finally,the optimized parameters using improved ACA were applied to a high performance flight simulator.The experimental results demonstrate that the NLPID controller has strong robustness against noise.This proposed ACA-based HLPID parameters optimization strategy can also be used to develope other simulation servo system′controllers.