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	基于在线支持向量回归更新策略的自适应非线性预测控制方法(英文)
	王平 ; 杨朝合 ; 田学民 ; 黄德先 ; Ping Wang ; Chaohe Yang ; Xuemin Tian ; Dexian Huang
	2016-03-30 ; 2016-03-30
关键词	Adaptive control Support vector regression Updating strategy Model predictive control TP13 TP181
其他题名	Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy
中文摘要	The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately.The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.; The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately.The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.
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=123456789/147144]
专题	清华大学
推荐引用方式 GB/T 7714	王平,杨朝合,田学民,等. 基于在线支持向量回归更新策略的自适应非线性预测控制方法(英文)[J],2016, 2016.
APA	王平.,杨朝合.,田学民.,黄德先.,Ping Wang.,...&Dexian Huang.(2016).基于在线支持向量回归更新策略的自适应非线性预测控制方法(英文)..
MLA	王平,et al."基于在线支持向量回归更新策略的自适应非线性预测控制方法(英文)".(2016).

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