Neural regulator design for parabolic distributed parameter systems with constraints in control

Petrov, P.; Toshkova, Daniela; Filchev, T.

doi:10.1109/is.2010.5548393

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2013

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A fuzzy method was applied for robust reference‐tracking‐control design of nonlinear distributed parameter time‐delay systems in . The optimal control problem was solved by a feed‐forward neural network in . In and , a time/spatial‐separation‐based least squares support vector machine (LS‐SVM) model identification approach was proposed for unknown nonlinear DPSs, which can get a time/space separation and dimension reduction through Karhunen‐Loève (K‐L) expansion so that the LS‐SVM approach can be used to model the system dynamics in a low‐dimensional temporal domain.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control for Nonlinear Distributed Parameter Systems based on LS‐SVM

Ai¹,

Ye²

2013

Asian Journal of Control

View full text Add to dashboard Cite

This paper presents a novel model predictive control configuration for nonlinear distributed parameter systems based on least squares support vector machine. First, a data‐based modeling methodology for an unknown nonlinear distributed parameter system is introduced. Subsequently, the model predictive control framework based on the aforementioned model is presented at the measured point. To verify the effectiveness and feasibility of the control arithmetic, simulation results from a tubular reactor with the controller designed by the presented procedure are given.

show abstract

Section: Introductionmentioning

confidence: 99%