Generalized Predictive Temperature Control in Tubular Chemical Reactors by means of Proper Orthogonal Decomposition and Least Squares Support Vector Machine

Ai, Ling; Stankovic, John A.; Teo, Kok Lay; Deng, Liwei

doi:10.23919/ccc50068.2020.9189120

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Then, the LS-SVM method is used to identify the unknown parameters of the lowdimensional time-series. Detailed methodology used to construct the generalized predictive temperature control in the tubular chemical reactor [27] will be discussed, and the control constraints and stability analysis will also be considered in the paper. The GPC based on SVM method has been successfully applied to LPSs with nonlinear characteristics [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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

Deng

et al. 2021

Symmetry

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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.

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