高速成像系统中光圈电机的PI参数智能优化算法研究与应用
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摘要
高速成像系统在断路器线圈故障诊断中的良好效果得到人们的关注,但在检测过程中发现成像系统中光圈电机控制存在精度难以满足现场测试的问题,且PI参数调整繁琐。针对上述问题,本文提出电机控制系统PI参数自动寻优,得到系统PI参数的最优解,从而实现光圈电机的准确、自动控制。本文在传统基因遗传算法基础上,在选择算子、交叉算子以及变异算子的设计上,较传统的SGA做了改进。改进后算法以Schaffer函数求极值问题作为优化测试对象,在平均最佳适应度、平均适应度计算次数以及平均进化代数等指标上均得到改善。并提出IITAE2作为适应度评价方法,对PI参数进行优化。基于IITAE2算法,分别从仿真与实验的角度进行验证,结果表明IITAE2指标遗传算法优化效果更好,可获得性能更优良的PI参数。
Due to its many advantages,the traditional PID strategy has been the dominant control method in the field control. One of the key issues of PID control is the PID parameters tuning and optimization,which is always a tedious trial-and-error procedure. To reduce the number of fitness calculation,the SGA was improved,and Schaffer function was taken as an example to verify the effectiveness of the improved SGA proposed in this paper. Three fitness evaluation methods were proposed to analyze the parameters optimization results. Both the simulation and experimental results show that the control system using PID parameters optimized by IITAE2 has better dynamic performance,which verify the algorithm proposed in this paper.
Due to its many advantages,the traditional PID strategy has been the dominant control method in the field control. One of the key issues of PID control is the PID parameters tuning and optimization,which is always a tedious trial-and-error procedure. To reduce the number of fitness calculation,the SGA was improved,and Schaffer function was taken as an example to verify the effectiveness of the improved SGA proposed in this paper. Three fitness evaluation methods were proposed to analyze the parameters optimization results. Both the simulation and experimental results show that the control system using PID parameters optimized by IITAE2 has better dynamic performance,which verify the algorithm proposed in this paper.
引文
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[3]何亚琴.数码摄影之景深、光圈和焦距的关系[J].轻松学电脑:电子乐园,2009(5):43-48.
[4]张溢,唐卫东,张俊林.一种新的优化算法--基因遗传算法的原理及在机械工程领域中的应用[J].成都航空职业技术学院学报,2005,21(4):3.
[5]罗国娟,杨再跃,李敏.基于遗传算法的自整定PI调节器及应用[J].吉林大学学报(工学版),2004,34(z1):3.
[6]张金林,幸南京,芮挺.改进PSO电液伺服系统的在线PI参数整定[J].控制工程,2008,15(2):4.
[7]席育棕.基于遗传算法的PI参数整定与优化[J].工程建设与设计,2005(8):2.
[8]毛敏,于希宁.基于遗传算法的PI参数优化方法[J].中国电力,2002,35(8):7.
[9]刘善军.基于扩展卡尔曼滤波器算法的永磁同步电动机无位置传感器控制的研究[D].西安:西安交通大学,2010.
[10]Bagis A.Determination of the PI Controller Parameters by Modified Genetic Algorithm for Improved Performance[J].Journal of Information Science and Engineering,2007,23(5):12.
[11]Qingfang T.Optimized PI Controller Via Genetic Algorithm[C].Seventh International Conference on Electronic Measurement,2005.
[12]张志刚,杜永贵.基于改进遗传算法的PI参数整定[J].太原理工大学学报,2005,36(4):4.
[13]Hu H.Optimal PI Controller Design in PMSM Servo System Via Particle Swarm Optimization[C].Thirty-First Annual Conference of the IEEE Industrial Electronics Society,2005.
[14]Li C.The Application of Immune Genetic Algorithm in PI Parameter Optimization for Level Control System[C].Automation and Logistics,2007.
[15]Cao J Y.Optimization of Fractional Order PI Controllers Based on Genetic Algorithms[C].Machine Learning and Cybernetics,2005Proceedings of 2005 International Conference on,2005.
[16]王静.基于改进的自适应遗传算法的锥形磁悬浮轴承控制器参数优化[D].西安:西安交通大学,2010.
[17]林洪涛.基于遗传算法的PI参数整定[J].沈阳工业学院学报,2004,23(4):4.
[18]蒋冬初.遗传算法及其在函数优化问题中的应用研究[D].长沙:湖南大学,2004.
[19]Kermiche S.Fuzzy PI Controller Parameters Optimized by Genetic Algorithms[C].3rd International Conference on Information and Communication Technologies,2008.
[20]Li D.Optimazation of Decenteralized PI/PI Controllers Base on Genetic Algorithms[J].Asian Journal of Control,2007,9(3):11.
[21]金芬.遗传算法在函数优化中的应用研究[D].苏州:苏州大学,2008.