|Table of Contents|

Application of nonlinear predictive control inthree-tank liquid-level control system(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年04期
Page:
439-
Research Field:
Publishing date:

Info

Title:
Application of nonlinear predictive control inthree-tank liquid-level control system
Author(s):
Sun JinggaoXue RuiYuan Wuyue
Key Laboratory of Advanced Control and Optimization for Chemical Processes,Ministry Education,East China University of Science and Technology,Shanghai 200237,China
Keywords:
multivariable predictive control nonlinear system unscented Kalman filter three-level control system
PACS:
TP273
DOI:
10.14177/j.cnki.32-1397n.2018.42.04.008
Abstract:
In order to further improve the calculation speed,control precision and stability of the nonlinear model predictive control,a controller design method based on the combination of Singular Value Decomposition Unscented Kalman Filter and Fast Ladder Predictive control is presented in this paper. Based on the Fast Ladder Predictive Controller,this method adds the Unscented Kalman Filter algorithm to reduce the noise interference and the control error. The controller design method is validated by the level control model of the three-tank water level. Simulation results prove that the method can improve the control precision and stability of the system under the noisy conditions,while ensuring the calculation speed.

References:

[1] Grüne L,Pannek J. Nonlinear model predictive control[J]. Process Automation Handbook,2011,3(3-4):43-66.
[2]Diehl M,Bock H G,Schl?der J P,et al. Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations[J]. Journal of Process Control,2002,12(4):577-585.
[3]郑涛,何德峰,陈薇,等. 快速阶梯式非线性预测控制[J]. 系统仿真学报,2007,19(22):5206-5209.
Zheng Tao,He Defeng,Chen Wei,et al. Fast stair-like nonlinear model predictive control algorithm[J]. Journal of System Simulation,2007,19(22):5206-5209.
[4]郑涛,吴刚,刘光宏. 分层多目标非线性预测控制[J]. 江南大学学报(自然科学版),2010,9(4):440-443.
Zheng Tao,Wu Gang,Liu Guanghong. Stratified multi-objective nonlinear model predictive control[J]. Journal of Jiangnan University of Science and Technology,2010,9(4):440-443.
[5]Pan Y,Wang J. Two neural network approaches to model predictive control[C]//2008 American Control Conference. Seattle,USA:IEEE,2008:1685-1690.
[6]Wang T,Gao H,Qiu J. A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control[J]. IEEE Transactions on Neural Networks & Learning Systems,2016,27(2):416-425.
[7]Chakrabarty A,Dinh V,Corless M J,et al. Support vector machine informed explicite nonlinear model predictive control using low-discrepancy sequences[J]. IEEE Transactions on Automatic Control,2016,62(1):135-148.
[8]冯凯,卢建刚,陈金水. 基于最小二乘支持向量机的MIMO线性参数变化模型辨识及预测控制[J]. 化工学报,2015(1):197-205.
Feng Kai,Lu Jiangang,Chen Jinshui. Identification and model predictive control of LPV models based on LS-SVM for MIMO system[J]. Journal of Chemical Industry and Engineering,2015(1):197-205.
[9]Yan Z,Wang J. Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks[J]. IEEE Transactions on Neural Networks & Learning Systems,2014,25(3):457-469.
[10]关长亮,王贵成. 基于误差补偿的谷氨酸发酵过程模型预测控制研究[J]. 沈阳化工大学学报,2016,30(1):70-75.
Guan Changliang,Wang Guicheng. Model predictive control based on error compensation for glutamic acid fermentation process[J]. Journal of Shenyang University of Chemical Technology,2016,30(1):70-75.
[11]张文,孙瑞胜. EKF与UKF的性能比较及应用[J]. 南京理工大学学报,2015(5):614-618.
Zhang Wen,Sun Ruisheng. Research on performance comparison of EKF and UKF and their application[J].Journal of Nanjing University of Science and Technology,2015(5):614-618.
[12]杨兆军,杨川贵,陈菲,等. 基于最小二乘算法和SVDUKF算法的电液伺服加载优化[J]. 吉林大学学报(工),2014,44(2):392-397.
Yang Zhaojun,Yang Chuangui,Chen Fei,et al. Optimization of the electro-hydraulic servo loading based on least square and SVDUKF algorithms[J]. Journal of Jilin University(Engineering and Technology Edition),2014,44(2):392-397.

Memo

Memo:
-
Last Update: 2018-08-30