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	间歇过程综合预测迭代学习控制方法基于二维理论的分析(英文)
	陈宸 ; 熊智华 ; 钟宜生 ; Chen Chen ; Zhihua Xiong ; Yisheng Zhong
	2016-03-30 ; 2016-03-30
关键词	Iterative learning control Model predictive control Integrated control Batch process Two-dimensional systems TP13
其他题名	Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory
中文摘要	Based on the two-dimensional(2D) system theory, 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) and model 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.; Based on the two-dimensional(2D) system theory, 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) and model 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.
语种	英语 ; 英语
内容类型	期刊论文
源URL	[http://ir.lib.tsinghua.edu.cn/ir/item.do?handle=123456789/147143]
专题	清华大学
推荐引用方式 GB/T 7714	陈宸,熊智华,钟宜生,等. 间歇过程综合预测迭代学习控制方法基于二维理论的分析(英文)[J],2016, 2016.
APA	陈宸,熊智华,钟宜生,Chen Chen,Zhihua Xiong,&Yisheng Zhong.(2016).间歇过程综合预测迭代学习控制方法基于二维理论的分析(英文)..
MLA	陈宸,et al."间歇过程综合预测迭代学习控制方法基于二维理论的分析(英文)".(2016).

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