Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Chen Chen; Zhihua Xiong; Yisheng Zhong

doi:10.1016/j.cjche.2014.05.008

Your Location：

Home >

Browse articles >

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

PROCESS CONTROL | Updated：2026-01-06

- Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory
- Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory
- 中国化学工程学报（英文版） 2014年22卷第7期页码：762-768
- Affiliations：
  
  Department of Automation, Tsinghua University,Beijing,China,100084
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.05.008
  中图分类号：
- 纸质出版：2014
- Accepted：
Scan QR Code
Chen Chen, Zhihua Xiong, Yisheng Zhong. Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory[J]. 中国化学工程学报（英文版）, 2014,22(7):762-768.

Chen Chen, Zhihua Xiong, Yisheng Zhong. Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory[J]. , 2014, 22(7): 762-768.
Chen Chen, Zhihua Xiong, Yisheng Zhong. Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory[J]. 中国化学工程学报（英文版）, 2014,22(7):762-768. DOI： 10.1016/j.cjche.2014.05.008.

Chen Chen, Zhihua Xiong, Yisheng Zhong. Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory[J]. , 2014, 22(7): 762-768. DOI： 10.1016/j.cjche.2014.05.008.

摘要

Abstract

Based on the two-dimensional (2D) systemtheory

an integrated predictive iterative learning control (2D-IPILC) strategy for batch processes is presented. First

the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then

an integrated framework of combining iterative learning control (ILC) andmodel predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function

the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type (Ptype) ILC despite the model error and disturbances.

关键词

Keywords

references

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Just-in-time learning based integrated MPC-ILC control for batch processes

Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model

Robust particle filtering-based nonlinear model predictive control: Application to PEMFC process