Design and real?time implementation of an adaptive fast terminalsynergetic controller based on dual RBF neural networks for voltagecontrol of DC–DC step?down converter

Type : Publication
Auteur(s) :  Badreddine BABES, Amar BOUTAGHANE, Noureddine Hamouda
Année :  2022
Domaine : Electrotechnique
Revue : Electrical Engineering
Résumé en PDF :  (résumé en pdf)
Fulltext en PDF :  (.pdf)
Mots clés :  Synergetic control (SC), Radial basis function neural network (RBFNN), · Fast terminal technique, Limited time, DC/DC step-down converter

Résumé : 

In this study, an improved Adaptive Fast Terminal Synergetic Controller (AFTSC) using Dual Radial Basis Function (RBF)Neural Networks (NNs) for output voltage control of an uncertain DC/DC step-down converters is proposed. Using theconsidered AFTSC, the with new manifold proposed here enables the DC/DC step-down converter’s state variables to trackthe preferred reference voltage in presence of disturbances from any initial condition with proper precision and limited time.To rendering the design more robust, a sort of dual RBFNNs are utilized to approximate in real-time unknown converternon-linear dynamics and reduce the modeling error without calling upon usual model linearization and simplifcations. Thestability of the closed-loop system is assured by means of the Lyapunov method. Considering the PWM DC–DC step-downconverter as an example, the considered adaptive RBFNN-FTSC law is studied in detail and implemented on a dSPACEds1103 card. All the simulation and experimental results illustrate the efciency and feasibility of the suggested controller.