期刊文献+

基于自适应遗传算法的PID参数优化仿真研究 被引量:5

Simulating Study on PID Parameter Optimization Based on Adaptive Gene tic Algorithm
下载PDF
导出
摘要 针对现有PID调节器的整定方法和遗传算法优化参数存在的问题,提出了一种自适应遗传算法用于PID参数寻优的方案。该算法采用了变群体规模和自动改变交叉概率、变异概率的措施,能提高算法的执行效率,收敛性较好,而且不易陷入局部最优解。以过热汽温控制系统为例,分别采用了简单遗传算法和改进遗传算法,对串级控制系统的PID参数寻优,仿真结果表明改进后的遗传算法具有较强的执行效率和很好寻优效果。 Aiming at the existing problems of current PID tuning methods and simpl e genetic algorithm,an adaptive genetic one is brought out to optimize PID param eters.The algorithm,adopting variable population size and adaptive crossover,mu tation probability, can better improve the execution efficiency,convergence and be difficult to fall into local optimization.Taking an example of optimizing PID parameters of cascade control system,the simulation results show that,comparing with simple genetic algorithm ,improved genetic algorithm have higer efficiency and perfect optimization effect. What's more, the algorithm and strategy is a a ll-purpose method and can be applied to many other optimization problems.
出处 《自动化与仪表》 2005年第1期30-32,共3页 Automation & Instrumentation
关键词 PID 改进遗传算法 变群体规模 串级控制系统 PID Improved genetic algorithm Variable population size cascade contr ol system
  • 相关文献

参考文献7

  • 1刘娜,韩璞,甄成刚.基于遗传算法的PID参数寻优[J].计算机仿真,2002,19(2):70-73. 被引量:28
  • 2李萌,沈炯.基于自适应遗传算法的过热汽温PID参数优化控制仿真研究[J].中国电机工程学报,2002,22(8):145-149. 被引量:102
  • 3韩瑛 朱希彦.自动控制系统数字仿真[M].中国电力出版社,1996..
  • 4Goldberg D E.Optimal Initial PopulationSize for Binary-Coded Genetic Algorithms [R].TCGA Report No85001.Tuscaloosa:University of Alabama,1985.
  • 5Simth R E. Adaptively Resizing Populations:An Algorithms and Analysis[Z].
  • 6Srinivas M ,Patnaik L M. Adaptive Probabilities of Crossover and Mutaiton in Genetic Algorithms [J].IEEE Transactions on systems,man and cybernetics,1994.
  • 7Strategy for Standing Reserve [J].IEEE Power System Management and Control , 2002.

二级参考文献12

  • 1韩璞,张丽静.热工过程控制系统参数优化方法的研究[J].华北电力学院学报,1993(1):50-57. 被引量:8
  • 2易继锴 侯媛彬.智能控制技术[M].北京:北京工业大学出版社,1998..
  • 3陈来九(Chen Laijiu).热工过程自动调节原理和应用(The theory and applications o f thermal process automation)[R]. 东南大学动力系资料(Data of Southeast Universit y Dept. of Power Engineering),1997,296-300.
  • 4Arabs J,Michalewicz Z,Mulawake J. GAVAPS-a genetic algorithms with varying population size[R]. The First IEEE Conference on Evolutionary Compution,Orland o,Florida,1994.
  • 5Srinivas M,Patnail L M. Adaptive probabilities of crossover and mutation in genetic algorithms[J]. IEEE Trans. Syst. ,Man, and Cybern. , 1994,24(4):656-6 67.
  • 6Hesser J,Manner R. Towards an optimal mutation probability for genetic algo rithms[R]. Proc 1st Conf on PPSN. 1990 .
  • 7Hajela P,Lin C L. Genetic search strategies in multicritrion optimal des ign[J]. Struct Optimization,1992,(4):99-107.
  • 8刘镇,姜学智,李东海.PID 控制器参数整定方法综述[J].电力系统自动化,1997,21(8):79-83. 被引量:57
  • 9刘乐星,毛宗源.水轮机的 GA-PID 控制器研究[J].电力系统自动化,1997,21(12):41-43. 被引量:25
  • 10王京,赵媛媛.一种改进的遗传算法用于PI控制器的参数寻优[J].北京科技大学学报,2000,22(1):93-96. 被引量:6

共引文献124

同被引文献37

引证文献5

二级引证文献6

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