The three-phase differential boost inverter (DBI) is a specialized category of power inverters that provides single-stage boosting functionality, characterized by high efficiency, cost-effectiveness, and compact size. However, it is challenging to control due to its nonlinearity and nonminimum phase characteristics. Specifically, when model predictive control (MPC) is applied, the nonminimum phase characteristics can lead to unstable internal dynamics. To solve this problem, this article proposes a novel continuous control set MPC (CCS-MPC) method for the three-phase DBI. Based on the established predictive models of circuit currents and voltages, the appropriate duty cycle for the next sampling period can be predicted. An additional inductor current-based stability constraint is designed to achieve system stability. This control strategy significantly optimizes the dynamic performance and the second-order harmonics in the output currents. Compared with finite-control set MPC, CCS-MPC features fixed switching frequency with higher steady-state accuracy and lower current ripple. Additionally, complex parameter design is avoided compared to proportional-integral-based control. Simulation and experiment are conducted to verify the efficacy of the proposed CCS-MPC for the three-phase DBI.

In software-based resolver-to-digital converters, parameter deviations of detected envelope signals (amplitude imbalance, dc offset, and nonorthogonal phase shifts) can introduce periodic rotor position estimation errors, leading to significant degradation of control performance of a permanent magnet synchronous machine. To address this issue, an online self-calibration method is proposed in the article. First, a self-calibration framework based on certainty equivalence principle is designed, and the effect of resolver parameter deviations on the harmonic components of envelope signals is analyzed. Then, the correlated harmonic components are separated to estimate parameter deviations. The rotor position estimation error is self-calibrated in the parameter deviations estimation process. Finally, the effectiveness of the proposed method is verified by simulation and experimental results.

More-electric aircraft increasingly employ dual-generator power systems to extract more electrical power from aircraft engines. In such systems, converter harmonics may superimpose on the common DC bus, introducing ripple and degrading power quality. Phase-shift–based harmonic cancellation is therefore required, but its achievable performance is constrained by harmonic prediction errors. This study proposes a neural network (NN)-based error correction method for harmonic prediction and cancellation in dual-generator power systems. The method compensates NN prediction errors and achieves improved harmonic cancellation performance under the considered operating conditions. Controller hardware-in-the-loop (C-HIL) testing is conducted under multiple load levels with both equal and asymmetric power sharing between the two channels. C-HIL results show that the proposed correction method can reduce the root-mean-square error of predicted harmonics by up to 99.43%, and then achieve the dominant switching harmonics cancellation of 53.29% on average relative to the original case under the considered operating conditions.

In this article, a fully soft switched high step up three level converter based on a Quasi-impedance source is introduced for use in photovoltaic applications. In the proposed converter, since an active switch is used instead of the diode in the impedance network, conduction loss associated with the diode is reduced, and zero-voltage-switching characteristics are provided in a wide range of output power for power switches, resulting in the improvement of efficiency. The proposed converter takes advantage of three level structure and it experiences much lower voltage stress on semiconductor elements compared to existing high step-up structures. This allows the use of mosfets with lower on the resistance, leading to reduced conduction losses and cost. Furthermore, the boost inductor in the input section not only maintains continuous input current, avoiding the need for bulky input capacitors, but also significantly optimizes power density and reduces cost. Other advantages of the proposed converter include high voltage gain with low duty cycle and the ability to turn off all diodes under ZCS condition. Experimental results from a 200-W laboratory prototype are provided to validate the proposed converter's performance.

This paper proposes an Improved Folding Model Predictive Control (IFMPC) strategy for Modular Multilevel Converters (MMCs) that significantly reduces computational complexity while enhancing control accuracy and real-time feasibility. Although a Finite Control Set MPC (FCS-MPC) uses actual capacitor voltages, the state-of-the-art methods rely on averaged capacitor voltages for prediction, which can introduce inaccuracies. The proposed IFMPC consistently employs instantaneous capacitor voltages to generate voltage vectors during the prediction stage, thereby reducing prediction errors and enhancing both transient and steady-state performance. The proposed approach integrates four critical control objectives AC current tracking, circulating current suppression, arm energy balancing, and leg energy distribution into a unified cost function with only two tunable weighting factors, simplifying the tuning process without compromising robustness. Real-time hardware-in-the-loop (HIL) validation on a scaled MMC prototype demonstrates rapid dynamic response, effective disturbance rejection, and reduced total harmonic distortion (THD) under various operating conditions, including parameter mismatches and grid harmonic distortions. Comparative analysis against existing indirect MPC techniques reveals that IFMPC achieves superior control performance with reduced computational burden, making it well suited for MMCs with a high number of submodules (SMs). The proposed method offers a scalable and industry-ready solution for advanced MMC control in high-power applications.

This article proposes a wireless power transfer system based on hybrid self-switching of coupled inductor that not only realizes constant current (CC)/constant voltage (CV) output but also effectively copes with a variety of abnormal working conditions in the charging process by switching on the secondary side. First, the LCC-LCC/S self-switching composite topology is presented, and the CC/CV switching is achieved by utilizing a coupled inductor with a center tap based on the LCC compensation topology. The working principle of the CC/CV topology and the parameter calculation flow are analyzed in detail. Subsequently, the impedance, output characteristics, and switching characteristics of the CC/CV topology are simulated and analyzed. In addition, the topology's stability under abnormal working conditions, such as the missing adjacency, load short-circuit, and load open-circuit, is analyzed. Finally, an experimental platform is constructed with the objective of verifying the feasibility and effectiveness of the proposed scheme. The experimental results demonstrate that the proposed charging system exhibits excellent CC and CV output characteristics without the need for additional inductor capacitance. The proposed system has a maximum output efficiency of 89.6%, a maximum current of 5.3 A, and a maximum voltage of 23 V, which fully meets the requirements of CC and CV wireless charging.

This letter introduces a three-phase multilevel converter for the integration of multiple battery submodules. The circuit comprises a synergetic modulated quasi-single stage design that includes a three-phase three-level voltage source converter operating at low switching frequency and two modular series-connected half-bridge converters operating with high switching frequency. Therein, all dc-link voltage rated devices switch at low frequency and zero voltage while sinusoidal currents are ensured without a complicated control. Furthermore, it provides a multilevel conversion and consequently smaller voltage transients enhancing power quality. In addition, the modular configuration enables flexible management of the series-connected battery submodules, eliminating the need for an extra balancer between the battery submodules. This letter provides an explanation of circuit operation and the synergistic modulation technique. Simulations and experimental results are presented to validate the feasibility of the proposed circuit.

This article addresses key technical challenges in switched reluctance motor (SRM) drives, notably significant torque ripple and the inherent tradeoff between efficiency and torque performance under varying operating conditions. A novel power converter with dual-source input (PC-DSI) characteristics is proposed, accompanied by a multimode control strategy (MCS) for enhanced operational flexibility. At the topological level, the proposed converter seamlessly transitions among four operating modes: low-voltage drive, high-voltage drive, high-voltage demagnetization with low-voltage drive, and a hybrid high-/low-voltage drives. The coordinated control of multilevel voltage outputs substantially improves dynamic regulation capability. In terms of control strategy, a multilevel multimode cooperative control method based on torque sharing is introduced. This approach establishes a multiobjective optimization model that balances efficiency and torque ripple minimization. By incorporating dynamic weighting coefficients alpha and beta , the system achieves online adaptive switching between efficiency- and ripple-prioritized control objectives. The experimental results demonstrate that the proposed scheme reduces torque ripple by up to 89% under rated operating conditions while improving the system efficiency by 10% across a wide speed range.

Owing to the unique shape and spatial constraints of autonomous underwater vehicles (AUVs), designing magnetic couplers presents significant challenges. Current researches on AUV coupler design has produced limited findings regarding the magnetic integration of arc-shaped coils, and there is a notable absence of systematic methods for optimizing parameter design. In terms of the aforementioned challenges, this article proposes arc-shaped magnetic integrated couplers designed for AUV wireless power transfer (WPT) systems, along with a multiobjective parameter optimization plan. The proposed magnetic integration scheme can achieve approximate decoupling among compensation inductors and between compensation inductors and the main coils. This design offers the benefits of reducing spatial leakage magnetic flux and significantly improving system power density. Furthermore, this scheme can be extended to other underwater high-order WPT systems. Subsequently, using output power and efficiency as optimization objectives and migration adaptability as one of the constraints, a multiobjective Salp Swarm Algorithm is applied. Finally, the proposed quasi-decoupled arc-shaped magnetic integrated coupler is applied to an underwater hybrid WPT system with input series and output parallel configuration, and the feasibility of the proposed scheme is validated through experiments. The experimental results demonstrate that, under a rated power of 1.2 kW, the peak efficiency of the system is 92.4%, and the allowable offset ranges in the axial, radial and angular directions are [-20 mm, 20 mm], [0 mm, 20 mm] and [-20 degrees, 20 degrees], respectively.

Accurate rotor time constant (RTC) is the key to achieving high-performance induction motor (IM) drives. To obtain an accurate RTC, a new robust online RTC estimation method based on harmonic current injection is presented. In the proposed method, the RTC is estimated from the speed response corresponding to the injected harmonic current. Compared with the existing schemes, the parameter mismatches will not influence the steady-state identification results of RTC. Thus, it has stronger robustness to parametric uncertainties. Finally, the effectiveness and correction of the proposed method are verified by simulations and experiments.

Direct model predictive control of electrical drives has gained significant acceptance within the research community over the past decade. To address the challenge of weighting factor (WF) tuning, one of the primary issues, remarkable efforts have been made. This paper compares several effective WF elimination methods, commonly referred to as decision-making (DM) methods, in the context of model predictive torque control for an induction motor. Each technique is assessed from multiple perspectives, including control performance, design complexity, and computational demand. Finally, the paper provides a comprehensive analysis of the effectiveness of DM methods.

The LCL-type three-level grid-connected inverter is extensively employed in photovoltaic (PV) power generation systems, which has multiple individually controlled objectives. To this end, an integrated division-summation (I-D- $\Sigma $ ) control strategy is proposed in this article, which can consider grid-connected current tracking, neutral-point potential (NPP) balance control, resonance suppression, and low-frequency common mode voltage (CMV), simultaneously. To compensate for the fact that the dual-division-summation (D-D- $\Sigma $ ) method only considers grid-connected current and resonance suppression, a virtual zero potential point is added for NPP balance control. Additionally, a low-frequency CMV estimation method based on fully connect-convolutional neural network (FC-CNN) is proposed, which perfectly avoids the unobservability of sensorless CMV. The I-D- $\Sigma $ method can effectively reduce the low-frequency CMV, which is compensated by the low-frequency CMV. The effectiveness of the proposed strategy was verified through comparative experimental results.

To solve the problems of high cost, large additional component size, large circulating loss, and complex control in existing auxiliary resonant soft-switching inverters, a three-phase passive auxiliary resonant pole inverter (ARPI) with symmetrical auxiliary networks and electric energy feedback function is proposed. On each phase bridge arm of the inverter, a set of passive auxiliary networks (PANs) without large electrolytic capacitors, transformers, and other devices is provided, which is capable of enhancing the power density of the inverter. Additionally, as the auxiliary commutation circuit does not employ auxiliary switches, it not only lowers the hardware cost but also simplifies the control complexity and enhances the reliability of the soft-switching inverter operation. The proposed topology is capable of enhancing the utilization ratio of DC voltage and reducing the harmonic content of the output waveform. According to the equivalent circuit diagrams in different operating modes, the soft-switching operating principle and realization conditions of soft-switching for the proposed ARPI are analyzed. The parameters of passive components are designed with the aim of minimizing the PAN loss. A 3-kW experimental prototype is constructed. The experimental results indicate that the switching tubes can realize the soft-switching action and suppress the rate of change of voltage and current. The efficiency of the prototype can reach 98.30% at the rated output power, which is a remarkable improvement compared with the same type of ARPIs.

Model Predictive Control (MPC) has emerged as an efficient alternative for the control of power converters. This paper presents a model predictive control strategy for a triple-output DC-DC converter that regulates 200 V, 5 V, and 25 V outputs for an off-grid DC microgrid. The system model incorporates state-space representation with coupling effects, while the control algorithm optimizes tracking errors and cross-coupling effects using a Finite Control Set Model Predictive Control (FCS-MPC) strategy. The simulation results demonstrate fast transient behavior with rise times of 0.937 ms, settling time of 3.94 ms, voltage deviations of (1 - 2%), a nearly constant switching frequency of 10 kHz, and steady-state error less than 0.1% during different load and parameter variations. These performance indicators validate the effectiveness of the proposed control approach in managing complex multi-output power conversion systems.

This paper presents a comparative evaluation of Proportional-Integral (PI) and Fuzzy Logic Control (FLC) strategies for regulating the output voltages of a Modified High-Efficiency Single-Input Triple-Output DC-DC converter (MHSTDC). The MHSTDC, designed to convert solar photovoltaic (PV) voltage into multiple stable DC outputs suitable for off-grid applications, features a non-linear high order topology, requiring appropriate control strategies to ensure voltage stability across high, medium and low outputs. Simulation results demonstrate that the FLC outperforms the PI controller in several key performance metrics, with improvements in rise time up to 21.1 % for the high-voltage output (0.9944 ms vs. 1.2614 ms) and 55.8% for the low-voltage output (1.5179 ms vs. 3.4348 ms). Furthermore, FLC reduces the voltage ripple by 36.53% for V-o1 (1.06% vs. 1.67%) and 47.9% for V-o2 (0.75% vs. 1.44 %), highlighting enhanced responsiveness and stability.

Permanent magnet auxiliary synchronous motor (PMaSynRM), as a new type motor, has been used in various power transmission applications. However, due to effect of the magnetic saturation and cross-coupling, for position sensorless control, the change of parameters leads to inaccurate of motor speed and position. This paper proposed a position sensorless control of motor parameter identification considering magnetic saturation and cross-coupling. Using hysteresis control by applying pulse voltage to dq axis separately and simultaneously, then calculate the flux linkage by integrating. Sampling the current and flux linkage, the parameters of magnetic model are calculated by the least square method, and the inductance and flux linkage model are obtained. Finally, it is applied to sensorless control of multi-signal-flux-observer. The accuracy and feasibility of the motor parameters identification results and sensorless control is verified by simulation.

The increasing integration of renewable energy, electric vehicles, and industrial applications demands efficient power converter control strategies that reduce switching losses while maintaining high waveform quality. This paper presents a Finite-Control-Set Model Predictive Control (FCS-MPC) strategy for three-phase, two-level voltage source inverters (VSIs), incorporating a secondary objective for switching frequency minimization. Unlike conventional MPC approaches, the proposed method optimally balances control performance and efficiency trade-offs by adjusting the weighting factor (λmin). Real-time implementation using the OPAL-RT platform validates the effectiveness of the approach under both linear and non-linear load conditions. Results demonstrate a significant reduction in switching losses, accompanied by improved waveform tracking; however, trade-offs in distortion are observed under non-linear load scenarios. These findings provide insights into the practical implementation of real-time predictive control strategies for high-performance power converters.

This article proposes a method to suppress electromagnetic force ripple in switched reluctance linear motors (SRLMs). While the force distribution function (FDF) method is effective, conventional approaches employing fixed functions limit adaptability to varying conditions, and existing adaptive algorithms often exhibit suboptimal online adjustment. To overcome these limitations, this study introduces an adaptive reference trajectory that dynamically adjusts based on mover speed and load. In addition, turn-on/off positions are adaptively modified according to a commutation point selected by the force-per-ampere rate; the selection of the commutation point reduces the current root-mean-square (RMS) and significantly enhances the tracking performance of the system. By transforming the Sigmoid function, the reference trajectory is defined by only two parameters, enabling easy online integration of the adaptive function. Furthermore, a multistep continuous control set model predictive controller (CCS-MPC) with self-correction is adopted as the current controller to improve tracking performance. Simulation and experimental results demonstrate superior regulation performance compared to genetic algorithm (GA) iterative methods and the conventional FDF approach. Compared with the GA algorithm, this method reduces the electromagnetic force ripple by an average of 11.3% and the average current RMS by 2.7%.

The dynamic nature of power systems combined with the need for low-latency and loss-tolerant communications, presents significant challenges to maintaining system reliability and resiliency. This paper proposes a novel integration of Finite Control Set Model-based Predictive Control with an extended prediction horizon and Software Defined Networked to address the resiliency problem and voltage/frequency deviations associated with traditional hierarchical microgrid. The communication framework integrates Software Defined Networked as a set of microservices distributed across local controllers and improved system reliability under communication constraints. The secondary control considers the variability of communication latency and packet loss to adjust the shared reference based on the spatial and temporal correlation. The microgrid is subjected to four test scenarios to analyze the impact of communications on distributed generation, plug-and- play capacity and load variations. The proposed control framework significantly improves system performance, achieving a 0.2-0.3 s recovery time, 0.05 s communication latency, and maintaining stability with up to 60% packet loss. Compared to hierarchical methods, it reduces recovery time by up to 90%, frequency deviation by up to 80%, and enhances power sharing and coordination between distributed generators. This method addresses the problem of low dynamic response of control strategies during disturbances, allowing the implementation of new, reliable and resilient hierarchical microgrids.

VIENNA rectifier has emerged as a promising topology for ac-dc power converter, but it has the problem of dc-link output voltage surge under no-load condition. In this article, a high-reliability hardware auxiliary circuit and an easy-implement software regulation algorithm are proposed to achieve voltage stability. First, voltage-oriented current cross-decoupling control strategy is introduced and the reason of the voltage surge has been analyzed using equivalent circuit model. Second, the equivalent circuit model of different working state for auxiliary circuit in association with VIENNA rectifier are described, along with introducing the selection of device parameters in the auxiliary circuit. Meanwhile, the mechanism of two working modes in the software regulation algorithm is analyzed to make the VIENNA work in boost and buck mode to achieve dc-voltage stability. Finally, the feasibility and effectiveness of the proposed methods have been verified using experimental results. The proposed methods not only suppress the dc-voltage surge but also exhibit a good performance in terms of supply-side power factor (PF) and dc voltage in a VIENNA rectifier, meanwhile maintaining the system adaptability under circumstance of the load step.