|本期目录/Table of Contents|

[1]吴 君,张雨飞,肖 晓.基于改进粒子群算法的隐式广义预测控制[J].工业仪表与自动化装置,2020,(01):8-12.
 WU Jun,ZHANG Yufei,XIAO Xiao.Implicit generalized predictive control based on improved particle swarm optimization[J].Industrial Instrumentation & Automation,2020,(01):8-12.
点击复制

基于改进粒子群算法的隐式广义预测控制

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2020年01期
页码:
8-12
栏目:
出版日期:
2020-02-15

文章信息/Info

Title:
Implicit generalized predictive control based on improved particle swarm optimization
作者:
吴 君张雨飞肖 晓
东南大学 能源与环境学院,南京210096
Author(s):
WU Jun ZHANG Yufei XIAO Xiao
School of Energy and Environment, Southeast University, Nanjing 210096, China
关键词:
隐式广义预测控制粒子群算法滚动优化自适应迁徙机制
Keywords:
implicit generalized predictive control particle swarm optimization rolling optimization adaptive migration mechanism
分类号:
TP273
DOI:
-
文献标志码:
A
摘要:
针对大多数工业系统的控制输入输出都存在约束的情况,提出一种基于改进粒子群算法的隐式广义预测控制算法(IGPC)。粒子群算法(PSO)是一种基于群体的智能优化算法,解决受约束的优化问题具有精度高、收敛速度快等优点;为了避免粒子群算法陷入早熟,提高精度,引入细菌觅食算法中的自适应迁徙机制。在隐式广义预测控制的滚动优化环节引入改进粒子群算法,弥补了传统GPC在处理受约束控制问题上的缺陷。仿真结果表明了该方法的有效性和良好的控制性能。
Abstract:
An implicit generalized predictive control(IGPC) algorithm based on improved particle swarm optimization(IPSO) is proposed for most industrial systems with control input and output constraints. Particle swarm optimization(PSO) is a swarm-based intelligent optimization algorithm, which has the advantages of high accuracy and fast convergence speed to solve constrained optimization problems. In order to avoid the premature and improve the accuracy of PSO, the adaptive migration mechanism in bacterial foraging algorithm is introduced. An improved particle swarm optimization algorithm is introduced to the rolling optimization of implicit generalized predictive control(IGPC),which makes up for the shortcomings of traditional GPC in dealing with constrained control problems.The simulation results show that the method is effective and has good control performance.

参考文献/References:

[1] Clarke D W,Mohtadi C,Tuffs P S. Generalized predictive control:part Ⅰ the basic algorithm[J]. Automatica,1987,23 (2): 137-148.

[2] Guzman J L,Berenguel M,Dorido S. Interactive teaching of constrained generalized prodictive control[J].Control Systems Magazine,IEEE(S0272-1708),2005,25(2):52-66.
[3] Demircioglu H. Constrained continuous-time generalized predictive control[J]. IEE Proceedings-Control Theory and Applications,1999,146(5):470-476.
[4] Wang Z,Sun Y. Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints[C]//Computational Intelligence and Natural Computing Proceedings(CINC):2010 2nd International Conference on. IEEE,2010:303-306.
[5] 蒋朝辉,李学明,桂卫华.大时滞系统全参数自适应预测控制策略[J].中南大学学报,2012,43(1):195-201.
[6] 肖本贤,朱志国,刘一福.基于粒子群算法混合优化的广义预测控制器研究[J].系统仿真学报,2007,19(4):821-824.
[7] 童朝南,肖磊,彭开香.基于遗传算法的结晶器液位约束广义预测控制[J].控制与决策,2009,24(11):1735-1739.
[8] 宋莹,陈增强,袁著祉.基于混沌优化的有约束广义预测控制器[J].工业仪表与自动化装置,2006(2):3-5.
[9] 李国勇.输入受限的隐式广义预测控制算法的仿真研究[J].系统仿真学报,2004,16(7):1533-1535.
[10] 吴密密,戴文战. 基于改进粒子群算法的约束隐式广义预测控制[J].浙江理工大学学报(自然科学版),2014, 31(3): 301-305.
[11] Kennedy J,Eberhart R. Particle Swarm Optimization[C]. Piscataway,NJ,IV:IEEE International Conference on Neural Networks(Perth Australia),IEEE Service Center. 1995:1942-1948.
[12] 吕振肃,侯志容.自适应变异的粒子群优化算法[J].电子学报,2004,32(1):416-420.
[13] 李国勇.智能预测控制及其MATLAB实现[M].北京:电子工业出版社,2005:309-324.

相似文献/References:

[1]毛璐明,杨玉群.考虑负荷重要性的微电网电源容量优化配置[J].工业仪表与自动化装置,2016,(06):62.
 MAO Luming,YANG Yuqun.Micro-grid generation capacity optimization with consideration of load importance[J].Industrial Instrumentation & Automation,2016,(01):62.
[2]崔丽洁,程换新,宋生建.基于改进PSO算法的模糊神经网络的研究与应用[J].工业仪表与自动化装置,2018,(05):24.[doi:1000-0682(2018)05-0000-00]
 CUI Lijie,CHENG Huanxin,SONG Shengjian.Research and application of fuzzy neural network based on improved PSO algorithm[J].Industrial Instrumentation & Automation,2018,(01):24.[doi:1000-0682(2018)05-0000-00]
[3]高敬更,李 项,张勇红,等.基于PSO算法的电动汽车电能计量优化[J].工业仪表与自动化装置,2019,(02):49.[doi:1000-0682(2019)02-0000-00]
 GAO Jinggeng,LI Xiang,ZHANG Yonghong,et al.An electric vehicle energy metering optimization based on particle swarm optimization[J].Industrial Instrumentation & Automation,2019,(01):49.[doi:1000-0682(2019)02-0000-00]

备注/Memo

备注/Memo:
收稿日期:2019-05-17
作者简介:吴君(1995),女,硕士研究生,研究方向为能源信息自动化。
更新日期/Last Update: 2020-01-10