PID控制参数现代设计技术的研究与应用
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摘要
本研究提供了一种切实可行而且较为优良的PID控制设计算法。建立了磁盘驱动系统的数学模型,丰富了磁盘驱动系统的理论体系。设计的基于三种不同的现代PID控制参数设计技术的磁盘驱动系统的PID控制算法有利于克服常规PID控制在磁盘驱动控制系统中存在的滞后和超调的缺点,从而提高PID控制算法的控制品质。
本研究是因为基于遗传算法的PID控制效果的不理想,促使我们用蚁群算法来寻找最优PID的控制参数。另一方面,为了提高磁盘数据访问速度和读写的可靠性,我们设计了基于三项现代技术的磁盘驱动系统的PID控制算法,实验结果证明了本文所提的算法的有效性。本文的主要工作和成果如下:
1.针对基于遗传算法的PID控制参数寻优的不理想,采用了蚁群系统和灰色系统理论,研究了基于蚁群算法的PID参数优化算法和灰色PID控制算法,实验结果表明:基于蚁群算法的PID参数优化算法无论是在最优解的质量方面还是在算法的执行效率方面都要优于基于遗传算法的PID参数优化算法,而采用了灰色PID控制的位置跟踪性能大大优于采用传统PID控制的位置跟踪性能,能够获得较好的控制跟踪效果和快速响应。
2.建立了磁盘驱动系统的数学模型,并将基于三项现代技术的PID控制算法应用到了磁盘驱动控制系统中,成功地设计出满足性能要求的磁盘驱动系统的控制器。
最后,对全文的研究工作进行了总结,并对蚁群算法的未来研究方向作了展望。
In this paper, a feasible and favorable algorithm is proposed for PID control. A mathematical model is established for the disk drive system, which enriches the theories of whole disk drive system. The PID control algorithms of the disk drive system, which is based on three different kinds of modern PID control design technologies, would benefit to overcome some issues arise in the accustomed PID control of disk drive system, such as lag and overshoot, and therefore improve the control quality.
It is well known that the quality of PID control based on genetic algorithm is not well satisfied, which motives us to make use of ant colony algorithm to optimize PID control parameters. On the other hand, to speed the rate of visiting data of disk and improve the reading and writing reliability, a set of PID control algorithms of disk drive system based on three kinds of modern technologies are designed, and whose effectiveness are finally demonstrated by some simulation examples. The main works and results of this paper as follows:
1. Since the optimal parameters of PID control cannot be searched by using genetic algorithm successfully, the optimized algorithms of PID control parameters based on colony algorithm and gray control algorithm, respectively, are proposed in virtue of the theories of colony system and the gray system. The simulation examples prove that the optimized algorithm of PID control parameters based on colony algorithm is better than one based on genetic algorithm both in the quality of optimized solution and implementation efficiency of algorithm; The performance of tracking position by employing gray PID control is much better than one by adopting accustomed PID control, and consequently the better effect of tracking control and rapid response are able to be achieved.
2. Establish the mathematical model for the disk drive system. Apply the PID control algorithms based on three kinds of modern technologies to the disk drive control system, and successfully design some controllers satisfying the given performance requirements for the diskdrive system.
Finally, the work of this paper is summarized and the prospective of future research is discussed.
本研究是因为基于遗传算法的PID控制效果的不理想,促使我们用蚁群算法来寻找最优PID的控制参数。另一方面,为了提高磁盘数据访问速度和读写的可靠性,我们设计了基于三项现代技术的磁盘驱动系统的PID控制算法,实验结果证明了本文所提的算法的有效性。本文的主要工作和成果如下:
1.针对基于遗传算法的PID控制参数寻优的不理想,采用了蚁群系统和灰色系统理论,研究了基于蚁群算法的PID参数优化算法和灰色PID控制算法,实验结果表明:基于蚁群算法的PID参数优化算法无论是在最优解的质量方面还是在算法的执行效率方面都要优于基于遗传算法的PID参数优化算法,而采用了灰色PID控制的位置跟踪性能大大优于采用传统PID控制的位置跟踪性能,能够获得较好的控制跟踪效果和快速响应。
2.建立了磁盘驱动系统的数学模型,并将基于三项现代技术的PID控制算法应用到了磁盘驱动控制系统中,成功地设计出满足性能要求的磁盘驱动系统的控制器。
最后,对全文的研究工作进行了总结,并对蚁群算法的未来研究方向作了展望。
In this paper, a feasible and favorable algorithm is proposed for PID control. A mathematical model is established for the disk drive system, which enriches the theories of whole disk drive system. The PID control algorithms of the disk drive system, which is based on three different kinds of modern PID control design technologies, would benefit to overcome some issues arise in the accustomed PID control of disk drive system, such as lag and overshoot, and therefore improve the control quality.
It is well known that the quality of PID control based on genetic algorithm is not well satisfied, which motives us to make use of ant colony algorithm to optimize PID control parameters. On the other hand, to speed the rate of visiting data of disk and improve the reading and writing reliability, a set of PID control algorithms of disk drive system based on three kinds of modern technologies are designed, and whose effectiveness are finally demonstrated by some simulation examples. The main works and results of this paper as follows:
1. Since the optimal parameters of PID control cannot be searched by using genetic algorithm successfully, the optimized algorithms of PID control parameters based on colony algorithm and gray control algorithm, respectively, are proposed in virtue of the theories of colony system and the gray system. The simulation examples prove that the optimized algorithm of PID control parameters based on colony algorithm is better than one based on genetic algorithm both in the quality of optimized solution and implementation efficiency of algorithm; The performance of tracking position by employing gray PID control is much better than one by adopting accustomed PID control, and consequently the better effect of tracking control and rapid response are able to be achieved.
2. Establish the mathematical model for the disk drive system. Apply the PID control algorithms based on three kinds of modern technologies to the disk drive control system, and successfully design some controllers satisfying the given performance requirements for the diskdrive system.
Finally, the work of this paper is summarized and the prospective of future research is discussed.
引文
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[2]Hang C C,Astrom K J.Refinements of the Ziegler-Nichols tuning formula for PID autotuners[A].Proc.Instrument Soc.Amer.Annu.Conf[C],1988:1021-1030.
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[6]Coon G A.How to set three-term controllers[J].Control Engineering,1956(3):71-76.
[7]Astrom K J,Hagglund T.PID Controllers:Theory,Design and Tuning[M].N.C.:Research Triangle Park,Instrument Society of America,1995.
[8]Astrom K J,Hagglund T.New tuning methods for PID controllers[A].Proc.European Control Conference[C].Rome,Italy,1995:2456-2462.
[9]Herrero J M,Blasco X,Martinez M,et al.Optimal PID tuning with gentic algorithms for non-linear process models[C].IFAC 15th Triennial World Congress,Barcelona,Spain:2002.
[10]宋胜利,左敦稳等.基于遗传算法寻优的PID控制技术及应用[J].系统工程理论与实践,2003(9):69-70.
[11]王小平.基于遗传算法的PID参数优化设计[J].自动化与仪表,1998,13(3):28-30.
[12]万佑红,李新华.用遗传算法实现PID参数整定[J].自动化技术与应用,2004(7):7-8.
[13]段海滨等.基于蚁群算法的PID参数优化[J].武汉大学学报,2004,37(5):97-100.
[14]尹宏鹏,柴毅.基于蚁群算法的PID控制参数优化[J].计算机工程与应用,2007,43(17):4-7.
[15]胡银全,何建平.灰色算法在PID控制中的仿真研究[J].四川工业学院学报,2004(增刊):95-96.
[16]马金山,高珍,杨洁明.灰色PID控制算法及仿真研究[J].机械管理开发,2004(5):17-19.
[17]Grochowski,R.Hoyt.Future trends in hard disk drives[J].IEEE Transactions on Magnetics,1996,3:1850-1854.
[18]Chris Wood,Paul Hodges.DASD Trends:Cost,Performance,and Form Factor[C].Proceedings of the IEEE,1993,81(4):573-584.
[19]张文修,梁怡.遗传算法的数学基础[M].西安:西安交通大学出版社,2000.
[20]虞蕾,赵红,赵宗涛.一种基于遗传算法的航迹优化方法[J].西北大学学报(自然科学版),2006,36(2):205-208,213.
[21]李辉,韩红,韩崇昭等.基于遗传算法的模糊逻辑控制器优化设计[J].西安交通大学学报,2002,36(4):385-389.
[22]朱伟兴,李新城,毛罕平,李萍萍,戚志东.基于遗传算法的模糊控制器的综合优化设计[J].计 算机工程与应用,2002,23:68-70,71.
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[24]李伟超,宋大猛,陈斌.基于遗传算法的人工神经网络[J].计算机工程与设计,2006,27(2):316-318.
[25]曹志松,朴英.基于混合遗传算法的航空发动机PID控制参数寻优[J].航空动力学报,2007,22(9):1588-1592.
[26]牛培峰,张君,关新平.基于遗传算法的混沌系统二自由度比例-积分-微分控制研究[J].物理学报,2007,56(7):3759-3765.
[27]李源,吴宏悦,吴杰.基于遗传算法PID整定的卫星姿态控制研究[J].中国空间科学技术,2007,4:66-71.
[28]张军,胡晓敏,罗旭耀等泽.蚁群优化[M].北京:清华大学出版社,2003.
[29]段海滨.蚁群算法原理及其应用[M].北京:科学出版社,2005.
[30]李十勇.蚁群算法及其应用[M].哈尔滨:哈尔滨工业大学出版社,2004.
[31]夏佳,张曦煌,沈玉方.一种基于蚁群算法的WSN路由算法[J].计算机工程与应用,2007,43(27):137-139.
[32]马昕,林丽清.蚁群算法在面向属性的数据约简中的应用[J].计算机仿真,2007,24(9):158-160.
[33]许智宏,孙济洲.用蚂蚁算法进行网格任务调度的研究[J].计算机应用,2005,25(10):2236-2237,2240.
[34]郭立俊,余晓芬.蚁群算法在系留气球PID控制中的研究与应用[J].计量与测试技术,2007,34(12):26-27,31.
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