Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints

被引：5

作者：

Ai, Ling ^{[1
,2
,3
]}

Xu, Yang ^{[1
]}

Deng, Liwei ^{[2
]}

Teo, Kok Lay ^{[3
,4
]}

机构：

[1] Harbin Univ Sci & Technol, Dept Automat, Harbin 150086, Peoples R China

[2] Harbin Univ Sci & Technol, Minist Educ, Key Lab Adv Mfg & Intelligent Technol, Harbin 150086, Peoples R China

[3] Curtin Univ, Sch Elect Engn Comp & Math Sci, Perth, WA 6845, Australia

[4] Sunway Univ, Sch Math Sci, Subang Jaya 47500, Selangor, Malaysia

来源：

SYMMETRY-BASEL | 2021年 / 13卷 / 03期

基金：

中国国家自然科学基金;

关键词：

multivariate generalized predictive control; parabolic distributed parameter systems; least squares support vector machine; control constraints; diffusion-reaction process; MODEL ORDER REDUCTION; PDE SYSTEMS;

D O I：

10.3390/sym13030453

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This manuscript addresses a new multivariate generalized predictive control strategy using the least squares support vector machine for parabolic distributed parameter systems. First, a set of proper orthogonal decomposition-based spatial basis functions constructed from a carefully selected set of data is used in a Galerkin projection for the building of an approximate low-dimensional lumped parameter systems. Then, the temporal autoregressive exogenous model obtained by the least squares support vector machine is applied in the design of a multivariate generalized predictive control strategy. Finally, the effectiveness of the proposed multivariate generalized predictive control strategy is verified through a numerical simulation study on a typical diffusion-reaction process in radical symmetry.

引用

页数：14

共 34 条

[1] Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models [J].