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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2021

02.01.2021

Online Support Vector Regression Based Adaptive NARMA-L2 Controller for Nonlinear Systems

Erschienen in: Neural Processing Letters | Ausgabe 1/2021

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Abstract

NARMA model is a simple and effective way to represent nonlinear systems, based on the NARMA model, NARMA-L2 controller is designed and has been successfully applied in the literature. Success of NARMA-L2 controller is directly related to the precision with which controlled systems’ dynamics can be estimated. In this paper, online SVR is utilized to obtain controlled plant’s subdynamics and consequently this information is used in the construction of NARMA-L2 controller. Hence functionality of NARMA-L2 controllers and high generalization capability of SVR are combined. Also, SVR formulates a convex optimization problem and therefore guarantees global optimum solution. The proposed method is assessed by performing simulations on a nonlinear CSTR system, the robustness of the designed controller is also tested under noisy and uncertainty conditions.
Vorheriger Artikel Design of Stochastic Passivity and Passification for Delayed BAM Neural Networks with Markov Jump Parameters via Non-uniform Sampled-Data Control
Nächster Artikel Political Optimizer Based Feedforward Neural Network for Classification and Function Approximation

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Metadaten
Titel
Online Support Vector Regression Based Adaptive NARMA-L2 Controller for Nonlinear Systems
Publikationsdatum
02.01.2021
Erschienen in
Neural Processing Letters / Ausgabe 1/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10403-8

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