Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/3/453 |
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doaj-db4dec68beb14d399caa5af0e7f9c2f52021-03-11T00:06:44ZengMDPI AGSymmetry2073-89942021-03-011345345310.3390/sym13030453Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control ConstraintsLing Ai0Yang Xu1Liwei Deng2Kok Lay Teo3Department of Automation, Harbin University of Science and Technology, Harbin 150086, ChinaDepartment of Automation, Harbin University of Science and Technology, Harbin 150086, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150086, ChinaSchool of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, AustraliaThis 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.https://www.mdpi.com/2073-8994/13/3/453multivariate generalized predictive controlparabolic distributed parameter systemsleast squares support vector machinecontrol constraintsdiffusion-reaction process |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Ling Ai Yang Xu Liwei Deng Kok Lay Teo |
| spellingShingle |
Ling Ai Yang Xu Liwei Deng Kok Lay Teo Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints Symmetry multivariate generalized predictive control parabolic distributed parameter systems least squares support vector machine control constraints diffusion-reaction process |
| author_facet |
Ling Ai Yang Xu Liwei Deng Kok Lay Teo |
| author_sort |
Ling Ai |
| title |
Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints |
| title_short |
Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints |
| title_full |
Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints |
| title_fullStr |
Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints |
| title_full_unstemmed |
Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints |
| title_sort |
least squares support vector machine-based multivariate generalized predictive control for parabolic distributed parameter systems with control constraints |
| publisher |
MDPI AG |
| series |
Symmetry |
| issn |
2073-8994 |
| publishDate |
2021-03-01 |
| description |
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. |
| topic |
multivariate generalized predictive control parabolic distributed parameter systems least squares support vector machine control constraints diffusion-reaction process |
| url |
https://www.mdpi.com/2073-8994/13/3/453 |
| work_keys_str_mv |
AT lingai leastsquaressupportvectormachinebasedmultivariategeneralizedpredictivecontrolforparabolicdistributedparametersystemswithcontrolconstraints AT yangxu leastsquaressupportvectormachinebasedmultivariategeneralizedpredictivecontrolforparabolicdistributedparametersystemswithcontrolconstraints AT liweideng leastsquaressupportvectormachinebasedmultivariategeneralizedpredictivecontrolforparabolicdistributedparametersystemswithcontrolconstraints AT koklayteo leastsquaressupportvectormachinebasedmultivariategeneralizedpredictivecontrolforparabolicdistributedparametersystemswithcontrolconstraints |
| _version_ |
1724226173277306880 |