期刊文献+

基于改进粒子群算法的PID参数优化方法研究 被引量:21

Study on Optimization of PID Parameter Based on Improved PSO
下载PDF
导出
摘要 针对标准粒子群算法的一些缺点进行了改进,提出了MWPSO优化算法,即Multi-Weight PSO。将MWPSO优化算法用几个标准测试函数进行测试,结果表明该算法优化结果的指标参数比标准PSO算法有所提高。在此基础上,用MWPSO优化算法对PID控制中的参数进行优化并将结果与遗传算法的结果进行比较,优化结果在保证PID控制稳定性基础上提高了PID控制的精度,且编码简单、易于实现,具有较好的应用前景。 Aiming at the disadvantage of classical PSO algorithm, this paper improves PSO algorithm and puts forward a new algorithm named Multi-Weight PSO. It uses the MWPSO to optimize the standard test functions and analyzes the test results, and finds the test results are better than before. Basing on these test results, it uses the MWPSO to optimize the PID parameters and finds the result of MWPSO is better than GA. This method makes it possible to improve the precision of PID control without influencing its stability. Also this method makes codes simpler and easier to realize, which means a better prospective.
出处 《计算机工程》 EI CAS CSCD 北大核心 2005年第24期41-43,共3页 Computer Engineering
基金 国家自然科学基金资助项目(60474030)
关键词 粒子群优化算法 MWPSO优化算法 遗传算法 PD参数优化 Particle swarm optimization(PSO) MWPSO GA Optimized PID parameter
  • 相关文献

参考文献7

  • 1Eberhart R C, ShJ Ytflmi. Comparison Between Genetic Algorithms and Particle Swarm Optimization[C]. Annual Conference on Evolutionary Programming, San Diego, 1998. 被引量:1
  • 2Kennedy J, Berthart R. Particle Swarm Optimization[C]. In: Proc. of IEEE Int. Conf. on Neural Network.s, Perth, 1995: 1942-1948. 被引量:1
  • 3Shi Yuhui, Eberhart R C. Fuzzy Adaptive Particle Swarm Optimization[C]. In: Proc. of IEEE Int. Conf. on Evolutionary Computation. Seoul, 2001:101-106. 被引量:1
  • 4Shi Y, Eberhart R C. A Modified Swarm Optimizer[C]. IEEE International Conference of Evolutionary Computation. Anchorage,Alaska: IEEE Press, 1998-05. 被引量:1
  • 5Clerc M, Kennedy J. The Particle Swarm: Explosion, Stability, and Convergence in Multi-Dimension Complex Space[J]. IEEE Transactions on Evolutionary Computation, 2002, 16(1): 58-73. 被引量:1
  • 6Eberhart R, Shi Y. Particle Swarm Optimization: Development,Applications and Resource[C]. IEEE Int. Conf. on Evolutionary Computation, 2001:81-86. 被引量:1
  • 7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

二级参考文献8

共引文献101

同被引文献179

引证文献21

二级引证文献207

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