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。
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.
出处
《计算机应用》
CSCD
北大核心
2013年第4期960-963,共4页
journal of Computer Applications
基金
甘肃省自然科学基金资助项目(112RJZA023)
关键词
迭代学习控制
模糊TSK模型
遗传算法
iterative learning control
fuzzy Takagi-Sugeno-Kang（TSK） system
Genetic Algorithm（GA）
