2D-PID adaptive control method for time-varying batch processes

WANG Zhiwen; LIU Yi; GAO Zengliang

doi:10.11949/j.issn.0438-1157.20151861

您当前的位置：

首页 >

文章列表页 >

2D-PID adaptive control method for time-varying batch processes

更新时间：2025-12-10

- 2D-PID adaptive control method for time-varying batch processes
- CIESC Journal Issue 3, Pages: 991-997(2016)
- 作者机构：
  
  浙江工业大学过程装备及其再制造教育部工程研究中心,浙江,杭州,310014
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151861
  CLC： P273TQ02
- Online First：12 January 2016，
  
  Published：12 January 2016
- 稿件说明：
移动端阅览
WANG Zhiwen, LIU Yi, GAO Zengliang. 2D-PID adaptive control method for time-varying batch processes[J]. CIESC Journal, 2016, (3): 991-997.
DOI：

WANG Zhiwen, LIU Yi, GAO Zengliang. 2D-PID adaptive control method for time-varying batch processes[J]. CIESC Journal, 2016, (3): 991-997. DOI： 10.11949/j.issn.0438-1157.20151861.

摘要

针对间歇过程存在的参数时变问题

提出一种基于二维PID(2D-PID)迭代学习框架的自适应控制方法。首先

通过粒子群优化算法快速获取初始的2D-PID控制参数。在批次内

采用自调整神经元PID控制器对其进行在线自适应调节。进一步

考虑批次间的重复特性

通过PID型迭代学习控制

以利用历史批次的信息来修正当前批次的调节变量

最终提高控制性能。通过间歇发酵过程的仿真和比较研究

验证了所提出方法的有效性。

Abstract

An adaptive control method using the two-dimensional proportional-integral-derivative (2D-PID) iterative learning control (ILC) is proposed for batch processes with time-varying parameters. First

the particle swarm optimization method is utilized to initialize the parameters of 2D-PID. Then

an auto-tuning neuron PID (ANPID) controller is adopted to adaptively tune the process within the batch operation. Moreover

considering the repetitive nature of batch processes

the PID-type ILC is further used to capture the useful information in historical batches. Consequently

the controller performance can be gradually improved batch to batch. The effect of the proposed controller is verified through a simulated batch fermentation process.

关键词

Keywords

references

Views

344

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A dual-stream multirate GRU soft sensor modeling based on data chunk strategy

ESO-based action dependent heuristic dynamic programming control and its application in a four-stage CSTR process

Fault detection of catalyst loss in FCC disengager based on autoencoder and multi-scale symbolic transfer entropy

TTPA-LSTM soft sensor modeling for multi-sampling rate data

ONLSTM soft sensor modeling based on time series transfer and dual stream weighting

Related Author

ZHANG Zhen

SHI Xudong

WANG Fazheng

XIONG Weili

CHEN Xun

LI Dazi

ZHU Chunmeng

LI Zeng

Related Institution

School of Internet of Things Engineering, Jiangnan University

Key Laboratory of Advanced Process Control for Industry (Ministry of Education), Jiangnan University

College of Information Science and Technology, Beijing University of Chemical Technology

College of Artificial Intelligence, China University of Petroleum

State Key Laboratory of Heavy Oil Processing, China University of Petroleum

⁰