ABSTRACT This paper addresses the limited voltage output capacity when a two‐phase six‐switch inverter is applied to a two‐phase hybrid stepper motor and the issue of still insufficient voltage output capacity after applying a traditional over‐modulation strategy based on the minimum phase error principle. It proposes a dual‐mode over‐modulation strategy by further subdividing the two regions of the conventional strategy and dynamically employing truncation or switching to fundamental voltage vectors based on phase angle. First, the expressions of the action time of adjacent basic vectors and the fundamental amplitude of output voltage are derived under both the traditional over‐modulation strategy and the dual‐mode over‐modulation strategy. Second, the relationship between the reference voltage modulation ratio and both the output voltage modulation ratio and harmonic distortion rate is analyzed. Finally, a platform is built for simulation and experimental verification. Simulation and experimental results show that the proposed dual‐mode over‐modulation strategy improves the maximum output voltage modulation ratio from 0.958 to 1.041 compared to the traditional over‐modulation strategy and improves the voltage utilization by 8.7%.

This paper proposes a data-driven model-free robust predictive control strategy for parallel three-level NPC inverters based on finite control set model predictive control (FCS-MPC), focusing on the zero-sequence circulating current (ZSCC) problem under parameter mismatch conditions. A set of virtual voltage vectors with zero average common-mode voltage (CMV) is introduced to effectively suppress ZSCC without adding additional constraints to the cost function. Meanwhile, an Integral Sliding Mode Observer (ISMO) is integrated into the predictive control framework to enhance robustness and enable reliable control using only input–output data. Unlike existing studies that primarily consider ZSCC suppression under an ideal system, this work specifically addresses the practical scenario in which system parameters deviate from their nominal values. Even when ZSCC suppression strategies are employed, parameter mismatch can still lead to noticeable circulating currents, motivating the need for a more robust solution. Simulation and experimental results validate that the proposed approach achieves excellent current tracking, neutral-point voltage balance, and effective ZSCC suppression under parameter variations, demonstrating strong robustness and feasibility for practical applications.

In this paper, a modified hysteresis band logic-analog controller with capacitors balancing ability is proposed. A multi-function five-level grid-tied inverter is investigated, which can be used to inject power into the grid and act as an active filter, simultaneously. It is also capable of absorbing active power to act as a storage system using batteries on the DC link and reactive power compensation to some extent. The new hysteresis band controller has been implemented on a conventional 5-level converter, based on the current injection method. In addition, this controller is also capable of balancing the voltage of the capacitors by selecting appropriate switches related the desired voltage vector. The proposed controlling method acts in the time domain and no need for complex transformations; furthermore, it has a high processing speed due to the utilization of logic gates. Moreover, due to the lack of need for a microcontroller or DSP, there is no halt phenomenon or delay in the execution of software loops. In this paper, power injection into the grid and active filtering ability have been investigated. To verify the proper operation, the simulation results as well as the laboratory 350W prototype results are presented.

ABSTRACT The impact of common mode voltage (CMV) on induction motor life is significant under non‐sinusoidal terminal voltage. In the advent of matrix converters for multi‐phase drives pose a challenge not only to reduce CMV but also to maintain the load current total harmonic distortion (THD) within the acceptable limits. Hence, the matrix converter (MC) performance is attributed to CMV reduction, load current THD minimization and supply side UPF maintenance which together is difficult to attend without any trade‐off. This paper presents a three‐phase to six‐phase matrix converter ( MC) with virtual vector‐based predictive control. In the classical predictive control selective utilization of the available voltage vectors, could lead to CMV reduction at the cost of the increase in the load current THD. The proposed predictive control is derived with selective virtual voltage vector formation to reduce the CMV while maintaining the load current THD within the limits. The trade‐off between CMV and THD is achieved by using a decisive weighting factor in the cost function. The performance of the MC, such as efficiency improvement with supply side unity power factor (UPF) is also achieved. This control algorithm is executed using a low‐cost dsPIC processor and verified experimentally to validate the effectiveness of the proposed algorithm.

This paper proposes a current model predictive control strategy for the permanent magnet synchronous motor (PMSM) based on a novel sliding mode observer to reduce the cost of PMSM and ensure good tracking performance. A super twisting sliding mode observer (STSMO) is designed to address the issues of high-frequency chattering and noise sensitivity caused by the large positive gain of traditional SMO. The discontinuous effect of the traditional SMO switching function is introduced into the derivative of the control rate, and a smooth estimate of the back electromotive force (EMF) is obtained through integration. Replace the sign function with a sigmoid function with smooth continuity to further reduce the chattering effect. To enhance the dynamic performance of the PMSM current loop, a finite control set model predictive control (FCS-MPC) strategy is employed in place of the conventional PI controller. Within each sampling period, all possible switching states are evaluated, and the optimal one is selected and directly applied to the inverter. Additionally, a dual-vector model predictive current control (DVMPCC) method is adopted to reduce current ripple. This approach synthesizes a voltage vector with arbitrary magnitude and direction by combining two voltage vectors within each sampling period. Numerical results demonstrate that the proposed sensorless PMSM predictive current control method achieves high accuracy in speed estimation and excellent dynamic response performance.

The virtual-flux direct power control (VFDPC) technique is a sensorless control approach aimed at improving the performance of grid-connected power converters. The approach involves simulating the grid voltage and AC-side inductors similar to an AC motor drive system, a principle deriving from direct torque control (DTC). The basic idea of VFDPC is to indirectly estimate the voltage at the converter's input through the concept of virtual flux, enabling the real-time calculation of instantaneous active and reactive power without necessitating direct voltage measurements. An essential element of the VFDPC approach is the implementation of a lookup table, used as a decision-making tool that identifies the most suitable voltage vector (a particular output state of the converter) in accordance with real-time power conditions. This provides instantaneous and smooth control of power flow, leading to enhanced operational stability. This approach allows for continual optimization of the converter's output, enabling VFDPC to significantly decrease total harmonic distortion (THD) while preserving reliable steady-state and dynamic performance. Experimental validation demonstrates that incorporating real-time feedback into virtual flux estimates improves the precision of voltage prediction and the responsiveness of the power control system. Consequently, VFDPC exhibits enhanced adaptability for various grid and load situations, presenting an appropriate choice for current power systems that demand efficient, reliable, and sensorless operation.

A minimum voltage vector error modulation assisted by a current reference modification for the PMSM drive

The inherent complexity and nonlinear nature of the dynamic representation of an induction motor-operated centrifugal fan/pump (CFP) system hinders the systematic closed-loop control design for flow rate ([Formula: see text]) and pressure/head ([Formula: see text]) regulation, widely used electrical energy-efficient solution in fluid transport infrastructures and designed intuitively nowadays by practical electrical engineers under the implementation constraints of industrial AC drives. This paper derives the system model allowing direct application of analytical closed-loop control design methodologies, avoiding heuristic solutions and it succeeds in the linearization of an experimentally validated nonlinear six-order dynamic representation of the fan coupled with a scalar-controlled induction machine for an arbitrary operating point. It also reduces the model order via derivations in the stator voltage vector reference frame. The results are verified via simulations and experiments. The analytically derived control-oriented linearized model is of third order and relates small deviations of the stator voltage frequency to the corresponding small deviations of the [Formula: see text] and [Formula: see text], suitable for scalar induction motor regulation applications. The model is presented as block diagrams, in state-space representation and computed as transfer functions. Simulated step responses of the linearized and nonlinear models are close and in good agreement with experimental data. Overall, this paper, for the first time, linearizes and reduces the order of the experimentally validated nonlinear model of the fan-induction motor system accounting for the fan's own dynamics. The obtained model is suitable for analytical closed-loop control design by practical electrical engineers, with following implementation based on industrial AC drives.

This paper employs a four-leg inverter topology to mitigate the high cost and zero-sequence current suppression challenges associated with dual-inverter open-winding permanent magnet synchronous motor (OW-PMSM) systems. Building on this topology, an improved current hysteresis control strategy incorporating a switching-state lookup table is proposed to suppress switching frequency fluctuations and current ripple. The developed system maintains high DC-link utilization and low cost while addressing the modulation complexity of conventional vector control and the switching frequency instability inherent in traditional hysteresis control. The study establishes a mathematical model of the OW-PMSM, analyzes the voltage vector distribution of the four-leg inverter, and designs an enhanced hysteresis control algorithm. By utilizing a predefined switching table to regulate switching logic in real time, the strategy achieves fixed switching frequency and effective harmonic suppression while preserving the fast-response characteristics of conventional hysteresis control. The experimental results demonstrate that the proposed control strategy achieves superior performance, effectively suppressing current ripple and providing ample stability margin, thereby validating its feasibility and effectiveness for practical engineering applications.

ABSTRACT Although the model predictive control (MPC) with discrete space vector modulation (DSVM) can effectively mitigate current ripple, thus reducing torque ripple, the computational burden increases dramatically with candidate voltage vectors. In this paper, a light‐computational MPC with current enhancement for permanent magnet synchronous machines based on DSVM is proposed, aiming to decrease the computational complexity and further improve the performance of the MPC with DSVM. Different from the existing DSVM, the proposed DSVM synthesizes virtual voltage vectors using only four real voltage vectors. In addition, it is noteworthy that the duty cycles of three nonzero voltage vectors corresponding to the virtual voltage vector are also duty cycles of the inverter upper bridge arms, thus eliminating the inverter switching signal calculation. Based on the proposed DSVM strategy, a voltage vector preselection method is designed to reduce candidate voltage vectors by four. Thereafter, voltage vectors around the optimal voltage vector are extended to further enhance current performance. Experiments are conducted on a 2‐kW electric drive platform to verify the feasibility and effectiveness of the proposed method.

A three-phase induction motor Direct Torque Control (DTC) system based on an FPGA is intended to raise the performance and reliability of three-phase induction motors in safety-critical tasks, such as metro train traction, where a failure can cause service failure and costly equipment damage. The system integrates necessary DTC capabilities, including torque and flux estimation, hysteresis control, sector detection, and inverter switching logic, on one FPGA platform. The integration allows currents and voltages of the stator to be monitored in realtime by means of the Analog-to-Digital Conversion (ADC) to precisely calculate the torque and flux as the load varies. The most notable aspect of the system is that it has integrated fault-diagnosis that continuously checks the current signatures of electrical faults like open-phase and overloading. Fuzzy logic based classification scheme is an improvement in diagnostic accuracy that is achieved in uncertain or noisy circumstances. On fault, the system automatically varies the vectors of inverter voltages or activates a protective shutdown to stop the damage of the motor and guarantee the reliability of the system. FPGA-in-the-loop testing with MATLAB/Simulink was used to verify the system with a fault detection latency of about 50 0.u. This shows that it is capable of providing quick fault response and adaptive motor control. The DTC system, designed in the FPGA, is inexpensive and is very suitable to implement in mission-critical applications, especially in metro traction systems, where rapid fault detection and protection are required to ensure the safety of operation and the operation of the motor. This system can improve the resilience of the motors, as it offers real time fault diagnosis and prevention, which makes it a perfect fit in dynamic and high reliability settings, such as the mass transit and the industrial process.

Ultrasound imaging is one of the modern methods for detecting atherosclerotic vascular lesions. Goal. To evaluate the relationship between wall thickness, shear stress, and blood flow turbulence in atherosclerosis of the internal carotid artery. Research materials and methods. 32 healthy men and women aged 29 to 44 years (average age 32±2.1 years) and with internal carotid artery stenosis of less than 50% in 48 patients aged 43 to 64 years (average age 51±3.1 years) were examined. All studies were performed at rest on a Mindray Resona 7 ultrasound machine (China) equipped with a linear sensor (3–11 MHz) with Vflow software. The wall thickness, systolic blood flow velocity (Vs) before and after stenosis, wall shear stress (WSS) by vector analysis, and blood flow turbulence before and after stenosis were calculated. The change in the direction of the voltage vector during the cardiac cycle was described using the oscillation index (OSI). The intragroup correlation coefficient was calculated for measuring arterial tension, wall thickness, and blood flow turbulence. Results. The results of examination of patients with less than 50% internal carotid artery stenosis were analyzed. The thickness of the internal carotid artery wall and the percentage of plaque stenosis were assessed. The average arterial wall thickness ranged from 0.8 to 2.1 mm, the average value was 1.4±0.4 mm. Blood flow turbulence in stenosis after narrowing was almost 6 times higher than normal (P<0.01). The shear stress of the wall correlated with vascular wall thickness (r= 0.55) and blood flow turbulence (r=0.42). Conclusion. With internal carotid artery stenosis < 50%, the wall shear stress was higher than normal. The narrowing is accompanied by turbulence of blood flow in both the distal and proximal sections. An increase in the wall thickness of the internal carotid artery leads to a decrease in tension in the area of stenosis by more than 1.6 times compared with the norm. At the same time, the oscillation index in stenosis is increased by 8 times compared to the norm. The registration of wall shear stress using vector flow mapping is a new and promising direction for assessing pathology and assessing the initial form of vascular narrowing.

Predictor neural network-based model-free predictive control using virtual voltage vector for multiparallel power converters

Conventional finite control set model predictive control (FCS-MPC) for permanent magnet synchronous motor (PMSM) drives suffers from a fundamental trade-off: shortening the control period improves current tracking but increases switching frequency and losses. This paper proposes a hysteresis-based variable control period MPC (HBVCP-MPC) to break this compromise. Unlike methods like direct torque control (DTC) and model predictive direct torque control (MPDTC) that use hysteresis to select voltage vectors (VV), our approach first selects the optimal VV via a cost function that balances current tracking accuracy and switching frequency. Hysteresis on the dq-axis currents is then employed solely to dynamically determine the application time of this pre-selected VV, which defines the variable control period. This grants continuous adjustment over the VV duration, enabling superior current tracking without a proportional rise in switching frequency. Experimental results confirm that the proposed method achieves enhanced steady-state performance at a comparable switching frequency.

The Modular Multilevel Matrix Converter (M3C) has the potential to contribute to onshore grid frequency response by utilizing the electrostatic energy stored in its submodules. However, in the current offshore wind power domain, control schemes for M3C-based Low-Frequency AC transmission systems (M3C-LFACs) fail to effectively exploit the capacitor energy of M3C to provide adequate inertia support. Existing M3C controls are typically grid-following and thus suffer from stability issues under weak-grid conditions. To address this challenge, a dual-port grid-forming control strategy for M3C-LFAC systems is proposed, based on an energy synchronization loop. This approach enables phase-locked-loop-free synchronization between the M3C and the grid while establishing low-frequency link voltage vectors. Building on this foundation, an optimized energy utilization method for M3C total energy is introduced, featuring a two-stage preset curve to maximize the system’s inherent energy for frequency response. Under varying levels of grid load disturbances, the proposed scheme ensures that M3C-LFAC systems can provide optimal inertia support. Finally, simulation studies in MATLAB 2024b/Simulink validate the effectiveness and advantages of the proposed method.

A multivector direct model predictive control (DMPC) scheme is proposed for the dual three-phase permanent magnet synchronous machine (DTP-PMSM) drive system to achieve closed-loop control for both fundamental current tracking and harmonic current minimization. The proposed multivector DMPC scheme employs four active voltage vectors, including two large vectors and two basic vectors for implicit modulation. Moreover, the control optimization problem is formulated as a four-dimensional quadratic programming problem, which is suitable for real-time implementation. The proposed multivector DMPC scheme enables fast and accurate tracking of the fundamental current as well as effective suppression of harmonic currents in both the fundamental and harmonic subspaces. In addition, a Kalman filter observer is incorporated to enhance robustness against model uncertainties and disturbances. Experimental results on a DTP-PMSM test bench verify that the proposed multivector DMPC scheme effectively reduces torque ripple, improves current quality, and enhances both steady-state and transient performance of the system.

In the model predictive control of a five-phase permanent magnet synchronous motor (PMSM), obtaining weight coefficients through cumbersome calculations is problematic to achieve optimal system control performance. To address this issue, this paper proposes a cascade model predictive current control method for a 5-phase PMSM, employing the concept of cascade model predictive control. First, the mechanism by which the suggested scheme chooses the best voltage vector is analyzed. Then, by setting the mechanism precedence of the regulated chargeables based on the characteristics of the 5-phase PMSM, objective function design schemes without weight coefficients are proposed: a fundamental current optimization scheme employs a high-amplitude voltage vector to improve DC bus voltage utilization and dynamic response capability; and a harmonic current optimization scheme reduces stator current harmonics to reduce scheme noise and vibration.

ABSTRACTTwo‐level voltage source converters (2L‐VSCs) use a single DC‐link for renewable energy/storage integration but require multiple series connections, limiting power extraction under partial shading or cell mismatch conditions. A multilevel inverter with independently controlled DC terminals can mitigate this issue. This paper presents a cost‐effective split DC‐link configuration using a 10‐switch neutral‐point clamped converter (TS‐NPC) and its control strategy. This results in a saving of two switches and six antiparallel diodes compared to the conventional three‐level (3L) neutral‐point clamped converter (3L‐NPC) offering a split DC‐link. The proposed control technique addresses the effect of voltage imbalance across split DC‐links on the AC side using virtual voltage vector formation, allowing autonomous control of DC voltages in the preceding stage. This paper investigates the operation of the TS‐NPC under unequal voltages in a split DC‐link, detailing the space vector modulation principle, virtual vectors, and duty ratio calculation. A comprehensive discussion on autonomous split DC‐link voltage control is provided. Extensive results and comparative analyses across various factors, including total harmonic distortion (THD), power loss, switching ratings, voltage stresses, and cost are presented. The performance of the TS‐NPC is experimentally verified, with results presented to validate its effectiveness.

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains.

Parameter estimation for solar photovoltaic panels is a popular research topic in green energy. Model parameters can be used for fault diagnosis in solar panels. Artificial neural network (ANN) approaches have been developed to estimate the model parameters of solar panels. In this study, an ANN and Adaptive Particle Swarm Optimization (APSO) approach for model parameter estimation of solar panel is proposed. Load perturbation is injected at the output of the solar PV panel, and the load voltage and current time series are measured. The current and voltage vectors are used as inputs for an ANN, which is used as a classifier for the ranges of the model parameters. The population of the APSO is initialized according to the results of the ANN classifier, and the APSO algorithm is then used to estimate the model parameters of the PV panel. Simulations and experimental studies show that the proposed method has better performance than conventional PSO, and it requires a smaller number of generations to achieve an average parameter estimation error of less than 5%.