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Design and real-time implementation of an adaptive fast terminal synergetic controller based on dual RBF neural networks for voltage control of DC–DC step-down converter

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Abstract

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 the considered AFTSC, the with new manifold proposed here enables the DC/DC step-down converter’s state variables to track the 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 converter non-linear dynamics and reduce the modeling error without calling upon usual model linearization and simplifications. The stability of the closed-loop system is assured by means of the Lyapunov method. Considering the PWM DC–DC step-down converter as an example, the considered adaptive RBFNN-FTSC law is studied in detail and implemented on a dSPACE ds1103 card. All the simulation and experimental results illustrate the efficiency and feasibility of the suggested controller.

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Authors and Affiliations

  1. Research Center in Industrial Technologies (CRTI), P.O. Box 64, 16014, Cheraga, Algiers, Algeria

    Badreddine Babes, Amar Boutaghane & Noureddine Hamouda

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  1. Badreddine Babes
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  2. Amar Boutaghane
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  3. Noureddine Hamouda
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Correspondence to Badreddine Babes.

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Babes, B., Boutaghane, A. & Hamouda, N. Design and real-time implementation of an adaptive fast terminal synergetic controller based on dual RBF neural networks for voltage control of DC–DC step-down converter. Electr Eng 104, 945–957 (2022). https://doi.org/10.1007/s00202-021-01353-y

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