期刊文献+

基于蚁群算法的模糊控制规则的过滤简化 被引量:2

Filtering Fuzzy Control Rules Based on Ant Colony Algorithm
下载PDF
导出
摘要 针对设计高维模糊控制器过程中会遇到的“规则爆炸”问题,利用蚁群算法进行控制规则的过滤简化。为了用尽量少的规则得到尽可能好的控制效果,利用蚁群算法在解决组合优化问题中的强大优势,在已有的完备规则中优选出若干条规则嵌入模糊控制器。采用带有时间窗口的蚁群算法去克服遗传算法优选模糊控制规则时可能产生的规则不连续的问题。该文还从遗传算法和蚁群算法工作机制的角度分析了对这两种算法加入约束条件的可操作性。以单级倒立摆控制系统为对象进行仿真研究,最后的仿真结果表明该文方法可以使模糊控制规则具有更好的简化效果和鲁棒性,并能具有好的控制效果。 To solve the typical "fuzzy rule explosion" problem in designing the high dimensional fuzzy controller, ant colony algorithm is used to filter a designed complete fuzzy - rule base. In order to get better control results with less control rules, ant colony algorithm is used, because of its superiority in solving combination optimization problems, to select several "good" fuzzy rules from a complete fuzzy rule base which is used to construct the Fuzzy controller. Ant colony algorithm with "time window" is used to overcome the problem of getting discontinuous fuzzy rules when selecting fuzzy rules with genetic algorithm. This article also analyzes the maneuverability of adding restriction in the two algorithms (GA and AS) from the mechanism point of view. At last, inverted pendulum is used as controlled plant to do the simulation. Results show that this method is effective enough to make the fuzzy controller much simpler and robust and to get good control performance.
出处 《计算机仿真》 CSCD 2006年第3期157-163,共7页 Computer Simulation
基金 哈尔滨工业大学校基金资助(HIT.2002.12)
关键词 蚁群算法 遗传算法 模糊控制规则 倒立摆 Ant colony algorithm Genetic algorithm Fuzzy rules Inverted pendulum
  • 相关文献

参考文献6

二级参考文献15

  • 1张晓缋,戴冠中,徐乃平.一种新的优化搜索算法──遗传算法[J].控制理论与应用,1995,12(3):265-273. 被引量:96
  • 2MAMDANI E H. Application of fuzzy algorithms for control of simple dynamic plant [ J]. Proceedings IEE, 1974,121 (12) : 1585 - 1588.
  • 3PASSINO K M, YURKOVICH S. Fuzzy Control [ M ].Beijing: Tsinghua University Press, ADDISON- WESLEY, 2001.
  • 4Dorigo M, Maniezzo V, Colomi A. The ant system: Optimization by a colony of cooperating agents[J ]. IEEE Transactions on Systenas. Man,and Cybernetics-Part B, 1996,26(1 ) :29 - 41.
  • 5Dorigo M,Gambardella L. Ant colony system: A cooperative learning approach to the traveling 'salesman problem[J ]. IEEE Transactions on Evolutionary Computation, 1997,1 (1) : 53 - 66.
  • 6Colorni A, Dorigo M, Maniezzo V, et al, Ant system for job-shop scheduling [ J ], Belgian Journal of Operations Research, Statistics and Combuter Science, 1994,34(1 ) :39 - 53.
  • 7Gianni Di Caro, Marco Dorigo. AntNet: Distributed stigmergetic control for communications networks [ J ]. Journal of Artificial Intelligence Research, 1998, (9) :317 - 355.
  • 8Thomas Stutzle, Holger Hoos. MAX-MIN ant system and local search for the traveling salesman problem[A]. Proc IEEE International Conference on Evolutionary Computation(ICEC'97) [C]. Indianapolis: [s. n. ], 1997. 309 - 314.
  • 9段广仁.线性系统理论[M].哈尔滨:哈尔滨工业大学出版社,1998..
  • 10张晓缋,1995年中国控制会议论文集,1995年,1352页

共引文献42

同被引文献18

引证文献2

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部