计算机应用 ›› 2013, Vol. 33 ›› Issue (04): 960-963.DOI: 10.3724/SP.J.1087.2013.00960

• 人工智能 • 上一篇    下一篇

基于遗传算法的模糊迭代学习控制算法

郝晓弘,金亚蓉,马宇,李恒杰   

  1. 兰州理工大学 电气工程与信息工程学院,兰州 730050
  • 收稿日期:2012-10-24 修回日期:2012-11-19 出版日期:2013-04-01 发布日期:2013-04-23
  • 通讯作者: 金亚蓉
  • 作者简介:郝晓弘(1960-),男,甘肃泾川人,教授,主要研究方向:学习控制、电机控制、现场总线、机器人;金亚蓉(1987-),女,甘肃榆中人,硕士研究生,主要研究方向:学习控制;马宇(1989-),男,甘肃兰州人,主要研究方向:电气工程;李恒杰(1981-),男,陕西户县人,博士,主要研究方向:学习控制、多目标优化。
  • 基金资助:

    甘肃省自然科学基金资助项目(112RJZA023)

Fuzzy iterative learning control based on genetic algorithm

HAO Xiaohong,JIN Yarong,MA Yu,LI Hengjie   

  1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou Gansu 730050, China
  • Received:2012-10-24 Revised:2012-11-19 Online:2013-04-01 Published:2013-04-23
  • Contact: JIN Yarong

摘要: 为了提高被控系统的控制精度及加快迭代域内的收敛速度,提出一种基于遗传算法的模糊PD型迭代学习控制算法。该算法通过模糊TSK模型设计迭代学习控制器,TSK模型中THEN部分的未知参数由遗传算法离线计算确定,进而产生合理的迭代学习律。针对被控系统,设计相应的迭代学习控制算法进行仿真分析,并与传统PD型迭代学习控制算法、模糊PID迭代学习控制算法相比较,进而将该算法用于双关节机械手进行仿真研究,仿真结果表明该算法的有效性。

关键词: 迭代学习控制, 模糊TSK模型, 遗传算法

Abstract: In order to improve the control precision and to speed up the convergence rate of the controlled system, a kind of fuzzy PD type iterative learning control algorithm was put forward based on genetic algorithm. In the proposed approach, the iterative learning controller was designed by fuzzy Takagi-Sugeno-Kang (TSK) system, the parameters of fuzzy TSK system were calculated by genetic algorithm, and then appropriate updating law was created. Appropriate iterative learning control algorithm of controlled system was designed and compared with PD iterative learning control algorithm and fuzzy PID iterative learning control algorithm, and then the proposed algorithm was used in double joint manipulator simulation. The simulation results show the effectiveness of the proposed algorithm.

Key words: iterative learning control, fuzzy Takagi-Sugeno-Kang (TSK) system, Genetic Algorithm (GA)

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