Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy

Ping Wang; Chaohe Yang; Xuemin Tian; Dexian Huang

doi:10.1016/j.cjche.2014.05.004

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Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy

PROCESS CONTROL | Updated：2026-01-06

- Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy
- Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy
- 中国化学工程学报（英文版） 2014年22卷第7期页码：774-781
- Affiliations：
  
  1. State Key Laboratory of Heavy Oil Processing, China University of Petroleum,Qingdao,China,266580
  2. College of Information and Control Engineering, China University of Petroleum,Qingdao,China,266580
  3. Department of Automation, Tsinghua University,Beijing,China,100084
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.05.004
  中图分类号：
- 纸质出版：2014
- Accepted：
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Ping Wang, Chaohe Yang, Xuemin Tian, 等. Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy[J]. 中国化学工程学报（英文版）, 2014,22(7):774-781.

Ping Wang, Chaohe Yang, Xuemin Tian, Dexian Huang . Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy[J]. , 2014, 22(7): 774-781.
Ping Wang, Chaohe Yang, Xuemin Tian, 等. Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy[J]. 中国化学工程学报（英文版）, 2014,22(7):774-781. DOI： 10.1016/j.cjche.2014.05.004.

Ping Wang, Chaohe Yang, Xuemin Tian, Dexian Huang . Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy[J]. , 2014, 22(7): 774-781. DOI： 10.1016/j.cjche.2014.05.004.

摘要

Abstract

The performance of data-drivenmodels relies heavily on the amount and quality of training samples

so itmight 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.

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