.png){kind=icon}
返回
Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jprocont.2011.09.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a novel multi-loop nonlinear internal model control (IMC) strategy for multiple-input multiple-output (MIMO) systems is presented under the partial least squares (PLS) framework, which automatically decomposes the system into several univariate subsystems in the latent space. To formulate a nonlinear dynamic PLS framework, we propose an ARX-neural network (ARX-NN) cascaded structure, and incorporate it into PLS inner model. A gradient-based optimization approach is then provided to identify the parameter sets of the ARX-NN PLS model so that the plant-model mismatch is minimized. Furthermore, with perfect model, we show that the response of the closed loop system can be reduced to a simple linear IMC filter with the original system delay. The simulation results of a methylcyclohexane (MCH) distillation column from Aspen Dynamic Module, demonstrate the effectiveness of our approach in terms of disturbance rejection and tracking performance. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Partial least squares
ARX-NN structure
Nonlinear IMC scheme
Aspen Dynamic Module
MCH distillation column
期刊
IF:
3.9
论文数:
3.5K
被引数:
7.3K
机构
引用论文
分享收藏Dynamic neural networks partial least squares (DNNPLS) identification of multivariable processes
PREAI
分享收藏Pituitary gland metastasis from rectal cancer: report of a case and literature review.png)
SpringerPlus
OAAI
SpringerPlusIF0
OAAI分享收藏分享收藏Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control ArchitecturesEnergies
OAAI
EnergiesIF0
OAAI分享收藏Multi-loop adaptive internal model control based on a dynamic partial least squares model基于动态偏最小二乘模型的多回路自适应内模控制
PREAI
分享收藏
查看更多内容