Nov 2026· IEEE transactions on power electronics· Vol 41, pp. 18934-18945· 0 citations· 23 references
Abstract
Additional on-time control in boundary conduction mode (BCM) boost power factor correction (PFC) circuits has been a practical solution to improve power quality. However, optimal on-time profiles are difficult to obtain using conventional model-based approaches due to nonlinearities in both hardware and control. These nonlinearities require extensive analytical modeling, parameter extraction, and tuning, which leads to a complex development process. Data-driven on-time control can estimate accurate on-time without analytical modeling because it inherently includes hardware nonlinearities. However, conventional approximation methods, such as lookup tables and polynomial fitting have limitations in terms of memory requirement and computation time. Although artificial intelligence (AI)-based methods have recently been used in power-electronics applications to reduce the burden of model analysis and optimize, the application of AI to additional on-time control in BCM boost PFC converters remain limited. This article proposes a lightweight neural network (NN)-based on-time control method using pretrained NN, without requiring analytical modeling. A complete design flow—data collection, training, and real-time deployment—is presented. Since the proposed NN can be rapidly retrained with newly collected data, it can be easily adapted to different device selections or circuit parameter variations without extensive redesign effort. Experimental results from a prototype with 110–220 VRMS input and 400 V/150 W output validate the effectiveness of the proposed NN controller in achieving competitive performance in terms of power factor and total harmonic distortion of the input current with the real time application on a low-cost microcontroller unit.
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
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.
Milovan Majstorović, B. Brkovic, L. Ristic et al.· IEEE Open Access Journal of...· 0 citations
In this study, an Artificial Neural Network (ANN)-based adaptive DC-link voltage (Vdc) controller is developed for a Parallel Hybrid Active Power Filter (PHAPF). The proposed controller aims to simultaneously determine the DC-link reference voltage (Vdc_ref) and the PI controller gains (Ki, Kp) as a function of the operating conditions. Training data for the ANN are obtained from simulations performed in the MATLAB/Simulink environment. Simulations performed using this training set show that adapting the DC-link reference voltage reduces total harmonic distortion (THD) compared to a PHAPF with a fixed Vdc_ref and reduces the DC-link voltage at low-power loads, which has the potential to lower switching losses, while adaptive PI gains improve transient behavior after large load changes. Therefore, the two adaptive quantities affect complementary aspects of performance. The trained ANN model is coded in the C programming language and implemented on a microcontroller-based control card. A 5 kVA PHAPF system is designed and fabricated for experimental verification. 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. In addition, reducing the DC-link voltage under low-power operating conditions has the potential to decrease voltage stress across the power switches and reduce switching losses. Furthermore, the proposed ANN-based adaptive DC-link voltage control algorithm exhibits better harmonic suppression performance despite the processing load and filtering delays.
LCL-type grid-connected inverters face problems including complex modeling, sampled current distortion from harmonics and negative-sequence components, and reduced control accuracy of conventional deadbeat predictive current control (DPCC) due to its heavy reliance on precise system parameters. To solve these issues, this paper proposes a model-free DPCC (MF-DPCC) using an adaptive-gain extended state observer (AGESO). Firstly, an ultra-local model is established to avoid dependence on accurate mathematical models. Secondly, an AGESO is designed to overcome conventional ESO drawbacks (initial differential peaking, inflexible bandwidth tuning, and tracking-noise immunity trade-off) by adopting adaptive gains to real-time estimate the ultra-local model’s lumped disturbance and state variables. Finally, a double second-order generalized integrator (DSOGI) purifies sampled currents and extracts fundamental positive-sequence components, reducing harmonic disturbance on the AGESO, allowing for higher bandwidth operation without excessive noise amplification, and indirectly enhancing resonance suppression by including LCL resonance-induced disturbance in the lumped term.
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 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
