|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.
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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:

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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