The integration of WBG based high frequency inverters with ultra-high speed Permanent Magnet Synchronous Machines (PMSMs) presents unique challenges in managing Electromagnetic Interference (EMI), crucial for ensuring electromagnetic compatibility (EMC). This work presents the design and validation of EMI filters for a high frequency WBG-based bi-directional grid-tie inverter, specifically developed for ultra-high-speed PMSM applications. Without EMI filter, the inverter cannot meet the conducted emission limits in the relevant standards. By Utilizing the grid and machine side filters, the system complies within the strictest category 1 limits of IEC61800-3 for use in residential areas. Test results confirm that the new filter significantly enhances electromagnetic compatibility, reducing EMI below industry limits while maintaining inverter efficiency and dynamic response. The inverter developed and experiments conducted on ultra-high-speed 180,000 rpm, 3 kW, with 400V DC link developed on both SiC and GaN switching at 100kHz. The work’s outcomes not only further the integration of high-speed PMSMs into smart grids but also pave the way for future innovations in power electronics design. This work is expected to facilitate the wider adoption of WBG technology in critical energy infrastructure.

Reliable control schemes are critical to ensuring converter operation in microgrids. This work proposes a robust adaptive sliding mode control for the three-level neutral-point-clamped power converter with multidisturbances. Specifically, an adaptive observer-based proportional (P) voltage controller is proposed to accurately and quickly regulate dc-voltage in real-time identifying unknown equivalent dc-loads, compensating for the active power reference. To track the reference, a disturbance observer-based integral sliding mode controller (ISMC) is adopted to dramatically enhance the power tracking performance in case of parameter mismatches and bias caused by current path changes and switch mode noise. In addition, a sliding mode observer coupled withPcontrol is established to balance dc-link (two) capacitors, rejecting harmonic injection and power ripple behaviors. Experimental data confirm that the proposed control scheme outperforms super-twisting observer-based ISMC, super-twisting algorithm, and proportional–integral control schemes in the transient/steady state operations in terms of dynamic response, grid current harmonic distortions, and robustness.

High penetration of power converters may lead to power ripple, voltage swings, and weak antidisturbances for microgrids. Confronting these issues, this work proposes a robust control scheme, discrete-time super-twisting observer (DSTO)-embedded quasi-integral sliding-mode control (QISMC), for a three-level neutral-point-clamped power converter system, dramatically enhancing power/voltage regulation performance and antidisturbance capability. A fast convergence DSTO is deployed to offset multidisturbances caused by parameter mismatches, unknown loads, current path changes, switch mode noise, and self-compensating power/voltage tracking biases in QISMC. To further mitigate power/voltage steady-state error and boost system robustness, a new quasi-integral sliding-mode surface is built, inherently improving power/voltage tracking performance. Experimental data confirm that the proposed control outperforms the discrete-time extended-state-observer-based QISMC, DSTO-based quasi-sliding mode control, and discrete-time proportional–integral control in power/voltage, grid current harmonics, and robustness.

This article presents two computationally efficient methods for selecting the optimal modulated voltage that can achieve superior dynamic performance for surface-mounted permanent magnet synchronous motors (SPMSMs). Specifically, when an SPMSM suffers a large reference or sudden load change, the controller might command a voltage reference, which is beyond the range of voltages that a modulator can synthesize. In such cases, the transient behavior of the motor can deteriorate when the demanded voltage is not properly limited to the voltage boundary. To address this issue, a simple overmodulation method based on common-mode-saturation injection (CMSI) is proposed. This strategy comes with very low computational cost and can easily find the voltage vector on the boundary, which is nearest to the reference voltage vector. Moreover, an alternative control method, referred to as quadratic program (QP) based deadbeat (DB) control, is proposed that also ensures optimal system performance during overmodualtion. According to this strategy, the control problem is formulated as a constrained QP, which is solved with an efficient solver based on an active-set method. Finally, extensive simulative and experimental investigations for an SPMSM are presented to demonstrate the effectiveness of the proposed overmodulation methods.

Three-level neutral-point-clamped (3L-NPC) power converters are necessary interfaces to form micro-energy systems. Naturally, designing a suitable control scheme, featuring superior dynamics, strong robustness, and simple structure, is a promising solution to guarantee more efficient operation of the converter. This article proposes a robust high-quality current control strategy for the 3L-NPC power converter in the stationary$\alpha \beta$frame. A super-twisting algorithm coupled with a Luenberger observer current controller is proposed to deal with the poor sinusoidal current tracking issue due to the existing inductance/grid frequency deviations and the disturbance of the sinusoidal dynamic nature. Additionally, an extended sliding mode disturbance observer-based proportional control is built to dramatically enhance the voltage regulation performance, in the case of capacitance deviations and unknown dc-loads. Experimental data confirm the effectiveness of the proposed solution outperforms the conventional proportional-resonant/-integral control in terms of accurate tracking current/voltage, antidisturbance, and grid current total harmonic distortion.

A Multisource Inverter (MSI) comprises several DC sources in the input that can be combined together with varying voltage levels to operate at different loads to reduce the battery size of electric vehicles. Different multisource inverter configurations have been presented recently in the literature. These structures use two DC sources to operate at different demand loads by using a low battery size. The weakness of these multisource inverters is that they use a high number of power switches. In addition, these structures cannot connect two DC used sources together in series to operate under a heavy load, which increases the battery's size, increases high power losses, and reduces efficiency due to a high number of switches. This paper proposes a new topology for multisource inverters that reduces the power electronics switches and the battery size due to generating four combinations between the two used DC sources in the proposed technique. The proposed multisource topology is controlled by the model predictive due to its popular advantages. The performance of the proposal is verified through simulation results in Matlab. The results show that the MPC is a good alternative for such applications due to its simplicity, high performance, and low harmonic content.

To eliminate the common-mode voltage (CMV) for matrix converters, this paper proposes a current sensorless model predictive control with reduced calculation overhead. In contrast to other traditional CMV-reducing methods which use all permissible switching configurations, this method synthesizes the output voltage and the input current with only six rotating vectors that lead to zero CMV. The proposed technique does not need to predict future load currents and source currents for those six rotating vectors, which provides another advantage in term of computation efficiency. Additionally, all current sensors are removed by using a Luenberger state observer instead in the control loop for cost reduction. The effectiveness of the proposed method is evaluated through simulation in different operation conditions.

Model Predictive Control (MPC) is becoming more popular than ever as an alternative to conventional modulations such as Space Vector Modulation methods to control matrix converters (MCs). However, the implementation of MPC is computationally expensive, because control objectives are required to evaluate all admissible switching states of the converter. Additionally, a large number of sensors to measure the 3-phase load currents, source currents, source voltages, and input voltages of MCs increases the overall cost. To sort this out, an efficient MPC is proposed for MCs to enable fast computation and low cost. This approach eliminates the calculations of future load currents and source currents for all possible switching states, requiring only two predictions for the calculation of output voltage and input current references. Further, it removes all current sensors by employing a Luenberger observer. A simulation study has demonstrated that the proposed method can reduce the computation overhead and hardware cost dramatically, leading to high-frequency operation and good converter performance.

The transformer-based inverters in PV systems increase the weight, size, and cost of the inverter while reducing efficiency. This research presents a new PV inverter topology to increase efficiency using a reduction of dc-link. The proposed multilevel inverter is comprised of six power switches, one discrete diode, and three capacitors to produce five voltage levels. The proposed inverter is connected to a PV panel at input and a local grid at output to inject a sinusoidal current waveform into the grid. To control the grid current, a finite set model predictive control is needed to evaluate the proposed inverter. A comparison study is carried out between the proposal and other five-level inverters to verify the strengths and weaknesses of the proposed multilevel inverter. Finally, to demonstrate the performance of the proposed multilevel inverter, the simulation results are presented in the MATLAB/Simulink environment.

This study presents current control algorithm based on a finite control set model predictive control (FCS-MPC) to achieve both high dynamics and current ripple suppression. In the proposed method, the smoothed voltage vectors with a finite set are applied as a control input candidate to avoid a sudden change in output voltage which generates large current ripple. In addition, the smoothness is determined automatically depending on a drive situation and system’s specification. Owing to this, fast transient response is achieved while keeping small current ripple during drive operation. The simulated and experimental results obtained with a permanent magnet synchronous motor (PMSM) show that the proposed method is effective for current ripple reduction and high dynamics control as compared to traditional FCS-MPC approach.

The Modular Multilevel Matrix Converter (M3C), also known as Triple Star Bridge Cells (TSBC) converter from the family of Modular Multilevel Cascade Converter (MMCC) has attracted attention in medium/high voltage, high power applications. Recent research has identified its potential for various direct high power AC/AC conversion applications due to its modularity, redundancy and high power quality. However, it suffers from cross-coupling while utilising low/fundamental frequency switching techniques such as Nearest Level Control (NLC) along with the double αβo transformation. This article proposes a decoupling approach to eliminate the coupling effect and low frequency oscillation at the output port for implementation of NLC in an MMCC-TSBC converter.

A finite-set predictive control (FSPC) approach is designed by considering the discrete-time model of the system under control. Therefore, variations of the model parameters and disturbances due to un-modeled dynamics deteriorate the performance of the FSPC. In order to overcome this problem, this paper proposes a FSPC strategy with an equivalent input disturbance (EID) observer for permanent-magnet synchronous generators (PMSGs) in wind turbine applications. The proposed EID observer estimates the total disturbance due to variations of the model parameters and un-modeled dynamics and considers it in the design of the controller. Experimental results are given to validate the performance of the proposed FSPC scheme.

The Siwakoti-H flying-capacitor inverter (sFCI) is a recent member of the family of transformerless inverters. Due to its minimal design, it presents a favorable alternative to conventional transformerless topologies. One of the major challenges in the control of the sFCI is to maintain the flying capacitor voltage within prescribed limits. To address this issue, a direct model predictive control (MPC) scheme is proposed for a single phase grid-connected sFCI. A discrete-time switched nonlinear model of the converter is derived, which captures the dynamics of the flying capacitor and the LCL filter. The nonlinear model enables the accurate prediction of the system behavior over the whole operating range. The proposed MPC strategy is tasked to work in two different modes, i.e., grid-disconnected mode and grid-connected mode, with specific control objectives. The presented results demonstrate the flying capacitor voltage control in grid-disconnected mode, and also illustrate the steady-state and dynamic performance of the controller in grid-connected mode.

In this work, the use of a multilayer perceptron feedforward neural network is proposed to capture the solution of the long-horizon finite control set model predictive control (FCS-MPC) problem in electrical drive systems. The motivation behind this research is based on treating the direct model predictive control problem of a power converter as a multi-class classification problem as it consists of a finite set of switching states, which can be seen as a finite number of different classes. By simulation results and hardware in the loop (HIL) test, it is proved that the solution of the long-horizon FCS-MPC can be captured by a real-time computationally implementable neural network that recognizes the converter switching states with an accuracy of 85 - 90%. Hence, it captures the performance enhancement of long horizon FCS-MPC in a computationally efficient manner (15.84 μs).

In this paper, an efficient finite control set model predictive control (FCS-MPC) for single-phase multilevel inverter (MLI) is proposed. The MLI topology under control reduces the number of the required semiconductor switches and produces more number of levels compared to traditional and recent topologies of MLIs. This topology has 49 different switching states, which means 49 predictions of the future current and 49 calculations of the cost function are required for each evaluation of the conventional FCS-MPC. Accordingly, the computational load is heavy. Thus, this paper presents a simplified FCS-MPC to reduce the calculation burden by computing the reference voltage and dividing the switching states of the MLI into two sets. Based on the reference voltage, one set is identified to determine the optimum voltage level. The proposed method does not require current predictions. Moreover, the number of cost function evaluations for each iteration is halved. As a result, the execution time is significantly reduced compared to that of the conventional FCS-MPC. The trade-off between the average switching frequency and the tracking performance is also investigated for the proposed scheme and compared to the conventional FCS-MPC. The effectiveness of the proposed solution in transient and steady state is verified by the simulation results.

In this paper the potential of the modulated model predictive control (MMPC) to control a matrix converter (MC) in the linear- and over-modulation zone is investigated. Input and output current references of MC are usually used to define the control objective in MMPC. By considering the predicted input and output currents of MC, a conventional space current vector modulation equation can be formed. As a result, control of the load and supply currents, good steady state performance and fixed switching frequency are achieved in the linear zone. Moreover, the transition time between the linear- and over-modulation modes is minimized by considering a new reference vector through a simple calculation. The feasibility of proposed method is demonstrated by simulation results and proved that the resulted controller includes the advantages of model predictive control (MPC) and space vector modulation (SVM) and effectively working in the different modes of operation.

In this paper, a flux linkage-based direct model predictive current control approach is presented for small permanent magnet synchronous motor (PMSM) drives. The method aims to minimize the current ripples at steady state by deciding on the optimal switching instant, while exhibiting fast dynamic behavior during transients. To this end, the future trajectory of the stator current is not computed based on the machine inductances or inductance look-up tables, but on the changes of the magnetic flux linkage by utilizing flux linkage maps. As shown, the proposed method can be particularly advantageous for electric drives with a noticeable nonlinearity in terms of saturation and/or cross-coupling effects since it allows for a significantly increased prediction accuracy, which leads to an improved steady-state performance as indicated by the reduced current distortions.

The Siwakoti-H flying-capacitor inverter (sFCI) is a potential candidate for photovoltaic applications, specifically for the transformerless grid-connected systems. One of the main challenges in the control of a sFCI is to maintain the flying capacitor voltage within prescribed limits while balancing the voltages on the three flying capacitors. This paper proposes an indirect model predictive control strategy for a three-phase sFCI connected to the grid via an LCL-filter. By linearizing the system model, the nonlinearities introduced due to the dynamics of the flying capacitor are neglected. Moreover, by not directly controlling the switches, but rather manipulating the modulating signal, the optimization problem can be formulated as a quadratic program (QP) and solved in a computationally efficient manner. The explicit solution computed by the controller makes the realtime implementation feasible by employing a carrier-based pulse width modulator (CB-PWM). The presented results illustrate the steady-state and dynamic performance of the controller.

In this paper we present a modified sphere decoder that can adjust the switching frequency in real time. To achieve this, the underlying integer least-squares (ILS) problem is reformulated and the lattice generator matrix is modified. By doing so, computational demanding operations required to be performed in real time are avoided altogether, rendering the proposed method computationally feasible. Moreover, the optimization process is kept computationally modest by appropriately manipulating the geometry of the ILS problem. The effectiveness of the introduced method is tested with a medium voltage variable speed drive system consisting of a three-level neutral point clamped (NPC) inverter and an induction machine.

In this study, direct speed control based on a finite control set-model predictive speed control (FCS-MPSC)with a voltage smoother is presented to reduce current ripple. In the proposed control scheme, the controller predicts the future current and speed states with a finite set of smoothed voltages and outputs the optimal smoothed voltage by using pulse width modulation (PWM). Because of this control scheme, a sudden change in the output voltage, which causes a large current ripple, is avoided. The simulated and experimental results obtained with a permanent magnet synchronous motor (PMSM), fed by a 2-level 3-phase inverter., shows that the proposed method effectively reduces the current ripple as compared with a standard FCS-MPSC.

This paper presents a simplified model predictive torque control (PTC) algorithm for surface-mounted five phase permanent magnet synchronous motors with non-sinusoidal back-EMF. The number of weighting factors is greatly reduced in comparison to conventional model predictive torque control. Three cost functions are evaluated in sequential manner to control the total torque and direct-axis currents. In the proposed method, the control performances can be adjusted by changing the selection criteria of the candidate voltage vectors, while it is done by tuning the weighting factors in conventional PTC. Simulation results have shown that, with the proposed method, predictive torque control of multiphase motor drives can be realized in a more simple and flexible way.

This paper presents a modified predictive current control (PCC) algorithm for five phase permanent magnet motor with non-sinusoidal back electromotive force (back-EMF), which reduces torque ripples and computational burden significantly. A cost function based on voltage tracking errors is proposed. One active voltage vector in combination with one zero voltage vector are applied to the inverter each sampling period. The switching instant is calculated analytically by solving a minimization problem of the cost function. The proposed cost function is evaluated 6 times each sampling period, thus the computational burden is greatly reduced. Simulation results show that torque ripples are greatly reduced and the currents are less distorted by using the proposed method.

In this paper, a new sensorless deadbeat control method is proposed. In the deadbeat method, the desired voltage is calculated via the model of the induction motor and inverter (prediction model). This voltage impels the motor to track the references of the torque and flux in the next control interval. Robustness is an important issue about the deadbeat method. Two new techniques are used to reach a robust speed-independent sensorless deadbeat method. A speed-independent model is sued for prediction. Therefore, the estimated speed will not be used in the prediction model. It will reduce the drift error problem. Also, a new adaptive predictive method is proposed for simultaneous estimation of the stator resistance and speed. Only direct-axis equation is used in the adaptive method. This will reduce the calculation burden. The new adaptive function is achieved via the Lyapunov technique. The stability of the multiple-input multiple-output system for simultaneous adaptation is analyzed for the gain design problem. Simulation and experimental results in wide range of speed are depicted in order to verify the proposed method.

Model predictive control (MPC) is a promising alternative for power electronics and electric drives. Research on fault diagnosis and fault-tolerant control for power converters with MPC is insufficient. Accurate fault diagnosis and fault-tolerant control solutions with easy implementation are very desirable. In this work we propose a novel current based fault diagnosis method of one-and two-phase open-circuit fault of machine-side converter in advanced back-to-back four-quadratic drive topologies, which is highly immunized from false alarm. The fault-tolerant control scheme can be easily embedded into normal MPC control algorithm. Simulation results validate the feasibility of proposed fault diagnosis and fault-tolerant control methods.

High voltage direct current (HVDC) transmission systems play an increasingly important role in offshore wind energy and long distance energy transmission systems. Modular multilevel converter (MMC) is an attractive topology for HVDC systems, due to its good modularity, scalability and inherent fault tolerant capabilities. For such topology, model predictive control (MPC) is a promising alternative. In particular, the long-horizon MPC provides better performances in terms of smaller THDs at very low switching frequency. However, its computational load is seen as a big challenge. In this paper we apply a long-horizon model predictive current control (MPCC) to a seven-level MMC-HVDC system. A switch and extrapolation and capacitor voltage sorting techniques are developed and combined to reduce the total computational burden. Performances of the proposed control strategy are evaluated with simulation results at a 65 MVA back-to-back 7L-MMC-HVDC configuration.

Finite control set model predictive control techniques have been emerged as good alternatives in particularly for multilevel and multiphase power converters, for which switching vectors with multiple magnitudes/directions are available but the modulator or switching table design becomes complex. In this paper, a finite-control-set model predictive direct torque and power control (FCS-DTC-DPC) for grid-tied three-level neutral-point clamped back-to-back power converters in permanent-magnet synchronous generator wind turbine systems is presented and experimentally compared with its counterpart: switching table-based direct torque and power control (ST-DTC-DPC). Both methods have been implemented and verified at a lab-constructed setup with a fully FPGA-based real-time controller. Experimental results confirm that both achieve (equivalently) good control dynamics, whereas FCS-DTC-DPC outperforms ST-DTC-DPC in terms of steady-state control performances at similar switching frequencies but has a higher computational demanding and is more sensitive to system parameter variations.

This paper presents a proportional-resonant (PR) controller for a quasi-Z-source inverter (qZSI) connected to a load via an intermediate LC filter such that it can be used as uninterruptible power supply (UPS) system. In order to improve the performance of the ac side of the qZSI, a PR controller is designed which compensates for selected low-order harmonics. By doing so, the output voltage of the UPS system can be kept sinusoidal not only for linear loads, but also for non-linear loads. For the dc side of the converter, a classical proportional-integral (PI) controller is used to adjust the capacitor voltage to its reference value. The mathematical models of both sides of the qZSI are derived and controllers are designed. In order to examine the qZSI performance under different kinds of loads, simulations are presented verifying the effectiveness of the proposed control strategy.

This paper presents a direct model predictive control (MPC) with an extended prediction horizon for the quasi-Z-source inverter (qZSI). The proposed MPC controls both sides of the qZSI based on the inductor current of the qZS network and the output current of the ac side. In order to improve the system performance, the MPC with extended prediction horizon is used. However, increasing the prediction horizon results in a huge increase in the computational burden which prevents the implementation of the MPC in real time. To solve this problem, two techniques are utilized, namely a branch-and-bound scheme and move blocking strategy. In this work, the discrete-time model of the qZSI is derived that accurately captures all operating modes and states. Then, the steady-state and transient operations of the qZSI with the proposed MPC are experimentally examined. The results confirm that by extending the prediction horizon, the qZSI behavior is significantly improved.

A new model predictive control method of electric drives with fixed switching frequency is presented. It proposes a numerical method based on circular dichotomy to calculate and select reference voltage vectors, which are given to pulse width modulator to generate switching signals. It is verified and compared with the existing dichotomy-based method in a predictive current controlled electric drive system with space vector pulse width modulation through simulation. Compared to existing method, the proposed method can significantly reduce code complexity and calculation effort without deteriorating system performance.

A modified model predictive control without cost function through deadbeat control for expected voltage calculation and Boolean logic for applicable voltage vector selection is proposed. Because of the absence of PWM and cost function, this method reduces simultaneously the system's hardware complexity and the calculation efforts as well as model dependency. The proposed method is verified and compared with the conventional predictive current control system of induction machine by simulation.

Sensorless ac drives have been widely adopted in many industry applications. However, the characteristic of strong multiple saliencies is still a main drawback impeding applying sensorless control over several types of machine, e.g., surface-mounted permanent magnet synchronous machine with concentrated windings (cwSPMSM). This work proposes a novel secondary saliency tracking (SST) algorithm to implement the sensorless control exclusively for such machines at low speed range. The saliency signals of a typical cwSPMSM under consideration are experimentally investigated. The stator background and physical mechanism of its strong multiple saliencies are explained in detail. Instead of processing the primary saliency signal, secondary saliency signal that has a better signal to noise ratio and more precise resolution is processed by a specially designed bandpass filter, and an adaptive notch filter for speed and rotor position estimation. Finally, the effectiveness and accuracy of the newly proposed SST method are verified by experimental results.

Surface-mounted permanent magnet synchronous machine with concentrated windings (cwSPMSM) is widely adopted in many industry applications. However, strong multiple saliencies is a main drawback impeding applying sensorless control over this type of machine. This work proposes a hybrid sensorless control scheme which integrates a novel Secondary Saliency Tracking (SST) algorithm and an improved Active Flux Observer (AFO) for cwSPMSMs. The saliency signals of a typical cwPMSM under consideration are experimentally investigated. Instead of processing the primary saliency signal, secondary saliency signal which has a better signal to noise ratio and more precise resolution, is processed by a special designed band pass filter for speed and rotor position estimation reaching a very low speed range with full-load. An improved AFO method with robust structure but effective performance is adopted for higher speed range estimation. The transient phase (switching between the SST and AFO) performance is guaranteed by a smooth transition region design, reaching a Hybrid Sensorless Control with wide speed range. Finally the effectiveness and accuracy of the newly proposed Hybrid Sensorless Control method are both verified by experimental results.

Direct Model Predictive Control (DMPC) is an attractive control method for power electronics and drives, characterized by straightforward concept, nice dynamics and great flexibility. However, relatively big ripples of the control variables and heavy computational efforts are regarded as two of the shortcomings. To cope with these, this work proposes a performance-enhanced DMPC concept with multi-predictions and lower computational efforts. The novelty of the proposed scheme is two-folds: i) By dividing each sampling interval into 3 prediction periods, the resolution of the control accuracy is therefore improved compared with the classical DMPC schemes with the same sampling period; ii) Instead of using the exhausting enumeration concept, a deadbeat notion is in-cooperated to find the equivalently optimal vectors and much lower computational efforts are therefore required. As a case of study it is here verified on a grid-tied two level Active-Front-End (AFE) and is realized using an entirely FPGA based solution. Compared with the classical DMPC schemes, better current/power qualities are achieved with the same sampling frequency. The effectiveness of the proposed scheme is emphasized with experimental results.

Single-switching-vector-per-sampling-interval character of the classical Direct Model Predictive Power Control (DMPPC) technique leads to big ripples of the control variables. Therefore, with a similar sampling frequency, its steady state performance is not satisfying. This work proposed a Revised Direct Model Predictive Power Control (R-MPDPC) concept for grid-tied AFEs. Instead of one single switching vector, two adjacent vectors are utilized to minimize a modified cost function based on time-optimal concept, which yields a synthesized equivalent voltage vector with an arbitrary phase. Therefore, more freedoms and precise tracking possibilities are included into the predictive controller design. The proposed control scheme is realized with a fully FPGA based solution on a lab-constructed AFE platform. Its performances are compared with the classical DMPPC with all experimental data. The results confirm that the control performances are greatly improved using the proposed R-DMPPC scheme.

Model-based predictive direct control methods are advanced control strategies in the field of power electronics. To control an induction machine (IM), the predictive torque control (PTC) method evaluates the electromagnetic torque and stator flux in the cost function. The switching vector selected for the use in the insulated gate bipolar transistors (IGBTs) minimizes the error between references and the predicted values. The system constraints can be easily included. The predictive current control (PCC) strategy assesses the stator current in the cost function. The weighting factor is not necessary. Both the PTC and PCC methods are very useful direct control methods that do not require the use of a modulator. In this paper, the PTC and PCC methods are carried out experimentally for an IM on the same test bench. The behaviors and the robustness in steady state and the performances in transient state are evaluated.

Optimal Pulse Patterns (OPPs) permit setting the switching frequency of power converters to a low value without compromising on Total Harmonic Distortion (THD). This paper presents the algorithm of calculating OPPs for Medium Voltage converters. The calculation procedure is explained in detail. Results are discussed and an efficient method is proposed to speed up and simplify calculations.

Field programmable gate array (FPGA) implementation of a model predictive control with constant switching frequency (MPC-CSF) for a permanent magnet synchronous machine (PMSM) is proposed. The basic finite states MPC (FS-MPC) can be combined with a pulsewidth modulation (PWM) modulator because of an effective cost function optimization algorithm in which voltage vectors are dynamically selected and calculated through iteration based on the idea similar to dichotomy. Using model-based design (MBD), MPC-CSF is implemented on an FPGA with parallel and pipeline processing techniques in short execution time. Functionality simulation analysis presents that MPC-CSF is much robust to parameter variations. Experimental results illustrate that MPC-CSF has good dynamic performance for PMSM drives.

An encoderless predictive torque control (PTC) is proposed in this paper. By using a rotor flux model reference adaptive system (MRAS) estimation method to PTC, the system has the virtue of low cost due to the absence of PWM and speed measurement components. PTC requires not only estimated speed but also estimated stator and rotor flux. In implementation, a compensated MRAS is considered for obtaining good flux estimations. The experimental results confirm that this control strategy has very fast dynamics, can adapt a very wide speed range, and shows good performance both at transient and steady states.

In this paper control systems suitable for a 4-leg NPC converter are discussed. The proposed control methodology is based on a finite set model predictive control. A theoretical analysis of the strategy, including tracking of non-sinusoidal currents and reduction of the switching frequency, is presented in this paper and experimental results, obtained with a 3 kw experimental prototype are discussed.

This paper presents an FPGA-based (Field Programmable Gate Array) sensorless controller for Surface Mounted Permanent Magnet Synchronous Machines (SMPMSM). A hybrid sensorless controller combining the signal injection technique and a linearly compensated flux observer is proposed. Using a Delta-Sigma A/D converter and FPGA oversampling technique, this work realizes a high performance high frequency (HF) injection sensorless control method which needs lower HF current response and introduces lower acoustic noises. The linearly compensated flux observer, based on back electromotive force (EMF) is used for sensorless control in the high speed range. The flux observer exhibits high dynamic and steady-state performance and is robust to parameter variation. Using model-based design, with the tools of MATLAB/Simulink and Simulink HDL (hardware description language) Coder, the whole control system is designed and implemented in a single chip. Experimental results demonstrate that the developed sensorless controller has high performance in the whole speed range.

This paper considers the use of back-to-back, full-bridge, three-level flying-capacitor converters in a series-input-parallel-output connected fashion for the implementation of a solid-state transformer. A finite-control-set model-based predictive control algorithm is developed for the control of the active rectifier front-ends. Pulse width modulation is used for the isolation back-ends. A solid-state transformer is constructed with two of the proposed back-to-back converters and experimental results are presented.

In this paper a novel two-step prediction Finite Control Set Model Predictive Control (FCS-MPC) strategy with weighting factor look up table and divided control interval is presented. The method can be applied for a two-level inverter because of its low torque ripple. The weighting factor in cost function is selected via a look up table which is based on torque ripple minimization. Two-step prediction method is combined with dividing the control interval in two parts: active time for applying the active voltage vectors (AVV) and zero time for applying the zero voltage vector (ZVV). By using this technique the prediction horizon is doubled without serious increase of calculation burden. Simulation and experimental results prove the validity of the proposed method in a wide range of speed.

This paper presents a pruning algorithm using adaptive pruning interval for system identification with general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The parameters are trained with the Levenberg-Marquardt (LM) optimization algorithm. Therefore the Jacobian matrix is required. The Jacobian is calculated by real time recurrent learning (RTRL). As both LM and OBS need Hessian information, computing time can be saved, if OBS uses the scaled inverse Hessian already calculated for the LM algorithm. This paper discusses the effect of using the scaled Hessian instead of the real Hessian in the OBS pruning approach. In addition to that an adaptive pruning interval is introduced. Due to pruning the structure of the identification model is changed drastically. So the parameter optimization task between the pruning steps becomes more or less complex. To guarantee that the parameter optimization algorithm has enough time to cope with the structural changes in the GDNN-model, it is suggested to adapt the pruning interval during the identification process. The proposed algorithm is verified simulatively for two standard identification examples.