Experimental Investigation of an Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller for Buck DC/DC Converters

Badreddine Babes, Noureddine Hamouda, Fahad Albalawi, Oualid Aissa, Sherif S. M. Ghoneim and Saad A. Mohamed Abdelwahab
Additional contact information
Badreddine Babes: Research Center in Industrial Technologies (CRTI), P.O. Box 64, Cheraga 16014, Algeria
Noureddine Hamouda: Research Center in Industrial Technologies (CRTI), P.O. Box 64, Cheraga 16014, Algeria
Fahad Albalawi: Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Oualid Aissa: LPMRN Laboratory, Faculty of Sciences and Technology, University of Bordj Bou Arreridj, El Anseur 34000, Algeria
Sherif S. M. Ghoneim: Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Saad A. Mohamed Abdelwahab: Electrical Department, Faculty of Technology and Education, Suez University, Suez 43533, Egypt

Sustainability, 2022, vol. 14, issue 13, 1-23

Abstract: This study proposes a way of designing a reliable voltage controller for buck DC/DC converter in which the terminal attractor approach is combined with an enhanced reaching law-based Fast Terminal Synergetic Controller (FTSC). The proposed scheme will overcome the chattering phenomena constraint of existing Sliding Mode Controllers (SMCs) and the issue related to the indefinite time convergence of traditional Synergetic Controllers (SCs). In this approach, the FTSC algorithm will ensure the proper tracking of the voltage while the enhanced reaching law will guarantee finite-time convergence. A Fuzzy Neural Network (FNN) structure is exploited here to approximate the unknown converter nonlinear dynamics due to changes in the input voltage and loads. The Fuzzy Neural Network (FNN) weights are adjusted according to the adaptive law in real-time to respond to changes in system uncertainties, enhancing the increasing the system’s robustness. The applicability of the proposed controller, i.e., the Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller (AFN-FTSC), is evaluated through comprehensive analyses in real-time platforms, along with rigorous comparative studies with an existing FTSC. A dSPACE ds1103 platform is used for the implementation of the proposed scheme. All results confirm fast reference tracking capability with low overshoots and robustness against disturbances while comparing with the FTSC.

Keywords: synergetic control (SC) law; fuzzy neural network (FNN) approximator; fast terminal synergetic controller (FTSC); finite-time convergence; DC/DC buck converter (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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