Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-7· 0 citations· 25 references
Abstract
This paper proposes an artificial neural network (ANN)-based control strategy for the bidirectional dual-input single-output (BDISO) DC-DC converter. The approach aims to combine the robustness of nonlinear control with the computational efficiency of data-driven implementation. A supertwisting sliding mode controller (STSMC) is first employed offline to generate representative state-action datasets that capture the converter's nonlinear dynamics under both buck and boost modes. The extracted state features and corresponding optimal switching actions are then used to train an ANN, enabling it to approximate the STSMC control law. During online operation, the trained ANN directly produces low-latency switching commands for voltage regulation, thereby eliminating the need for real-time STSMC computation. The proposed controller is evaluated in MATLAB/Simulink and benchmarked against conventional proportional-integral (PI) and fixed-frequency sliding mode control (FSMC) methods. Simulation results demonstrate that the ANN-based controller achieves superior transient response, precise voltage regulation, and reduced chattering, confirming its potential as a compact and efficient solution for multi-port DC-DC converter applications.
This paper describes an advanced algorithm for current control in a modular multilevel power converter based on deadbeat model predictive control, which is further enhanced by applying an artificial neural network.
Milovan Majstorović, B. Brkovic, L. Ristic et al.· IEEE Open Access Journal of...· 0 citations
The rapid growth of electric vehicles and energy storage systems requires efficient two-way power conversion systems, such as bidirectional VSIs operating in Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. Unfortunately, conventional Hysteresis Current Control (HCC) methods lead to unstable switching frequencies, degrading the system's power quality and performance. This study proposes a single-phase two-way Voltage Source Inverter (VSI) with a full bridge topology controlled by an Artificial Neural Network (ANN) based Adaptive Hysteresis Current Control (AHCC) scheme. ANN is used to define hysteresis bands adaptively to keep the switching frequency stable. The system consists of a two-way VSI connected to the grid and a two-way DC-DC converter that connects the battery via DC-Link. The simulation results showed that the proposed method produced a narrower instantaneous frequency switching range of 5.263 kHz-25 kHz compared to Fixed-HCC of 11,111kHz-50 kHz, so that AHCC-ANN produced an average frequency switching value of 13.37 kHz, close to the desired frequency switching of 15 kHz, while Fixed-HCC was 20,25 kHz. On the other hand, the% of THD for AHCC-ANN (2.72%) is lower than that for Fixed-HCC (3.2%). On the other hand, the DC-link voltage can also be maintained at 400 V during charging and discharging. These results show that AHCC with ANN can stabilize switching frequencies and DC-link voltages and support effective bidirectional power flow.
Ludviatul Amanah, F. Pamuji, Mochamad Ashari· International Seminar on Int...· 0 citations
In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based duty-cycle correction method is proposed for a non-isolated interleaved bidirectional DC–DC converter used in a hybrid photovoltaic (PV)–battery system. The ANFIS controller was developed using training data generated from an optimized conventional fuzzy-logic controller operating in both buck and boost modes. The main objective of the proposed control strategy is to produce a smoother duty-cycle response, improve transient behavior, and maintain better output-voltage stability than the conventional fuzzy-logic approach. The converter and control system were modeled and tested in MATLAB/Simulink under constant-voltage and constant-current operating conditions. The simulation results indicate that the proposed ANFIS-based controller improves the converter's dynamic response and provides smoother duty-cycle adjustment. In buck mode, the output-voltage ripple is reduced from 0.1181 V to 0.1051 V, indicating a modest improvement. A more significant improvement is observed in boost mode, where the voltage ripple decreases from 2.963 V to 1.348 V. The results indicate that the proposed ANFIS controller works effectively in boost mode, particularly given its greater sensitivity to duty-cycle changes, switching dynamics, and transient disturbances.
Yunifa Miftachul Arif, Saodah Omar, A. Ahmed et al.· Engineering, Technology &...· 0 citations
This paper presents the design and implementation of a robust adaptive control strategy for a Dual Active Bridge (DAB) converter used in More Electric Aircraft (MEA) applications. In MEA electrical architectures, a 270 V DC main bus must reliably supply regulated 28 V DC power for onboard systems such as avionics and battery subsystems. In response to this demand, a new controller based on a Fractional-Order Adaptive Super-Twisting Algorithm (FO-ASTA) integrated with a Model Reference Adaptive Control (MRAC) framework is proposed. The controller enhanced the robustness and chattering-free properties of super-twisting sliding mode control, the dynamic memory effects of fractional-order systems, and the real-time adaptability of MRAC.The proposed FO-ASTA controller integrated with the MRAC scheme is evaluated under steady-state conditions, load variations, reference changes, and parametric uncertainties. Moreover, its performance is compared with the Fractional-Order Sliding Mode Controller (FOSMC) and Fractional-Order Super-Twisting Algorithm (FOSTA) controllers. Simulation results on the circuit level confirm that the proposed controller outperforms the alternatives in terms of tracking accuracy, convergence speed, disturbance rejection, and control smoothness. This makes it a strong candidate for high-performance DC–DC conversion in next-generation MEA power systems.
Muhammad Arif Anwar, Wang Li, M. Balas et al.· INTERNATIONAL JOURNAL OF COM...· 0 citations
This paper presents a comprehensive review of control techniques, simulation mechanisms, and validation methods applied to DC-DC converters, with a focus on high-step-up topologies used in renewable energy systems such as photovoltaic and wind power applications. Control strategies including classical PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed in terms of performance, robustness, and implementation complexity. Simulation platforms and hardware-in-the-loop (HIL) validation frameworks are also discussed as key enablers for rapid prototyping. The findings reveal a clear trend toward intelligent and hybrid control schemes that combine nonlinear techniques with artificial intelligence to address the inherent nonlinearities and parametric uncertainties of DC-DC converters. However, challenges remain in real-time implementation due to computational demands, which drives the need for future developments focused on the (i) integration of AI-based controllers with low-cost embedded platforms, (ii) standardization of HIL-based validation workflows, and (iii) optimization of converter topologies for specific applications such as electric vehicle charging and photovoltaic grid integration. Looking forward, the convergence of advanced control algorithms, real-time validation platforms, and application-specific converter design is expected to define the next generation of power electronics systems, enabling more efficient, reliable, and scalable renewable energy integration.
Rafael Antonio Acosta Rodríguez, J. R. Rosero García, Marco Rivera· World Electric Vehicle Journ...· 0 citations
Experimental results demonstrate that the proposed ANN-based adaptive DC-link voltage control algorithm achieves lower total harmonic distortion (THD) than PHAPFs employing a constant Vdc_ref and exhibits better harmonic suppression performance despite the processing load and filtering delays.
