Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach

Author

Todorov, Yancho ; Terzyiska, Margarita ; Ahmed, Shehab ; Petrov, Michail

Author_Institution

Dept. of Intell. Syst., Inst. of Inf. & Commun. Technol., Sofia, Bulgaria

fYear

2013

fDate

19-21 June 2013

Firstpage

1

Lastpage

5

Abstract

It is proposed in this paper a study on the influence of the Levenberg-Marquardt optimization approach for computation of the control actions in Nonlinear Model Predictive Controller. To predict the future plant behavior, a classical Takagi-Sugeno inference is used. A comparison by applying the Gradient descent and the Newton-Raphson optimization approaches is made. The efficiency of the proposed optimization strategies is demonstrated by experiments in MATLAB environment to control a Continuous Stirred Tank Reactor.

Keywords

Newton-Raphson method; fuzzy control; fuzzy neural nets; fuzzy reasoning; neurocontrollers; nonlinear control systems; optimisation; predictive control; Levenberg-Marquardt optimization approach; MATLAB environment; Newton-Raphson optimization approach; Takagi-Sugeno inference; continuous stirred tank reactor; control actions; fuzzy-neural predictive control; gradient descent algorithm; nonlinear model predictive controller; optimization strategies; Computational modeling; Equations; Mathematical model; Optimization; Prediction algorithms; Predictive control; Predictive models; Gradient descent; Levenberg- Marcquart; Newton-Raphson; Nonlinear Predictive Control; Optimization; Takagi-Sugeno model;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on

Conference_Location

Albena

Print_ISBN

978-1-4799-0659-8

Type

conf

DOI

10.1109/INISTA.2013.6577624

Filename

6577624