2026· IEEE Open Access Journal of Power and Energy· Vol 13, pp. 619-627· 0 citations· 26 references
TL;DR
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.
Abstract
This paper describes an advanced algorithm for current control in a modular multilevel power converter. The proposed algorithm is based on deadbeat model predictive control, which is further enhanced by applying an artificial neural network. Rather than supplanting the existing predictive controller, the neural network is used to supplement it, thereby improving control performance. The reference current tracking capability is enhanced under all operating conditions, particularly in cases where the reactive elements of the converter exhibit a relatively high resistive component. By introducing the neural network, the inherent delay of model predictive control in such scenarios is virtually eliminated, which reduces the overall control error. The network is trained offline on a set of control inputs obtained by optimizing the current response in a wide range of operating modes. Algorithm verification is performed for a three-phase modular multilevel converter in different steady-state and transient conditions. A state-of-the-art high-fidelity real-time simulator is used in both the algorithm development and verification stages.
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.
Saeed Hosseinnataj, Majid Mehrasa, S. Taheri et al.· 2026 6th International Confe...· 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.
To address the requirements of current tracking control and power quality improvement for active power filters (APF), this paper proposes a nested terminal sliding mode control scheme based on hippocampal neural network to overcome the performance limitations of existing APF control methods. First, the circuit structure of the APF is elaborated, and the mathematical model including lumped system uncertainties is derived. Then, a nested terminal sliding mode surface is designed to ensure that the tracking error converges to zero in finite time, which achieves performance improvement compared with traditional linear sliding mode control that can only realize asymptotic convergence. Afterward, a hippocampal‐inspired neural network that mimics the human hippocampal information processing mechanism is introduced for the first time to learn the unknown nonlinear terms in the sliding mode controller, effectively weakening the adverse effects of system uncertainties on control performance. A novel feature selection mechanism is proposed to extract and process key information within the network, greatly reducing the network computational overhead. A dual‐loop structure is designed in the neural network to improve the processing efficiency of time‐varying harmonic signals, and the online adaptive update law of network parameters is derived based on Lyapunov theorem to guarantee system stability. Finally, simulation and hardware experimental results verify the effectiveness of the proposed algorithm. This method reduces the total harmonic distortion (THD) of the grid source current to 1.29% in simulation and 3.03% in experiment. Compared with mainstream methods, it exhibits excellent current tracking ability, strong robustness, and superior grid harmonic suppression performance.
Pengpeng Lyu, Qiangsheng Bu, Guangjian Li et al.· International Journal of Ada...· 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
This paper presents the design, modelling, and experimental implementation of a Cuk DC-DC converter regulated using Model Predictive Control (MPC). The Cuk converter is widely preferred because it provides both step-up and step-down voltage conversion while maintaining continuous input and output currents, which helps in reducing current ripple and improving power quality. However, the nonlinear behaviour of the converter and the presence of system constraints make its control challenging when using conventional PI or PID controllers, often resulting in poor transient response and limited robustness under disturbances. To overcome these limitations, an MPC-based control strategy is proposed. MPC utilizes a mathematical model of the converter to predict future system behaviour and determine the optimal control action by solving an optimization problem at each sampling instant while considering system constraints. The proposed controller improves reference tracking, enhances dynamic performance, and offers better disturbance rejection compared to traditional control methods. Experimental results obtained from the implemented Cuk converter validate the effectiveness of the proposed MPC approach. The results demonstrate faster transient response, accurate output voltage regulation, and strong robustness against load and input voltage variations, confirming the superiority of MPC for high-performance control of Cuk DC-DC converters.
N. Jeyaprakash, Kannan K, K. K· 2026 6th International Confe...· 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