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Modulated Model Predictive Control (MMPC) techniques can be applied to enhance matrix converter (MC) performance in the linear modulation zone (LMZ). However, extending the converter operation in the overmodulation zone (OMZ) remains a problem. A key limitation of existing control methods in the OMZ is using the duty cycles calculated for the LMZ without considering the voltage or current reference vectors. This approach can lead to inaccurate calculations and a slower transient response when switching between the two operational zones. To overcome this challenge, a novel reference vector calculation method is proposed in this paper. The proposed approach simplifies the duty cycle calculation and ensures optimal performance in the OMZ by introducing new vector calculations and dividing the OMZ into two subzones. This method also minimizes the transition time between LMZ and OMZs to maximize the usable operating range of MCs. Simulation and experimental results validate the proposed method outperforms two other MMPC methods in improving the MC performance, reducing the transition times between operational zones, and maximizing the converter utilization.

The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT-LeNet-5-LSTM technique in finding the complex characteristics in the data, while LSTM learns the trends in the dataset and performs the necessary analysis of vibrations occurring in faulty machines. The developed model was examined for various loads and faults to extract results having accuracies of 99.6%, 96.9%, 92.5% and 96.6% for load conditions 3, 2, 1, and 0, respectively. These results demonstrate the ability of the proposed model to adapt according to varying load conditions while having the necessary levels of accuracy. This validates the model to perform precise fault detection and diagnosis, offering capabilities of predictive maintenance in industrial settings.

Remotely sensed data are dominated by mixed land use and land cover (LULC) types. Spectral unmixing (SU) is a key technique that disentangles mixed pixels into constituent LULC types and their abundance fractions. While existing studies on deep learning (DL) for SU typically focus on single time-step hyperspectral or multispectral data, our work pioneers SU using MODIS MS time series, addressing missing data with end-to-end DL models. Our approach enhances a long-short-term-memory-based model by incorporating geographic, topographic (geo-topographic), and climatic ancillary information. Notably, our method eliminates the need for explicit endmember extraction, instead learning the input–output relationship between mixed spectra and LULC abundances through supervised learning. Experimental results demonstrate that integrating spectral-temporal input data with geo-topographic and climatic information significantly improves the estimation of LULC abundances in mixed pixels. To facilitate this study, we curated a novel labeled dataset for Andalusia (Spain) with monthly MODIS MS time series at 460-m resolution for 2013. Named Andalusia MultiSpectral MultiTemporal Unmixing, this dataset provides pixel-level annotations of LULC abundances along with ancillary information.

In pursuit of improving energy efficiency in the industry, this paper proposes two model-based loss-minimization techniques for synchronous reluctance motors (SynRM): the model-based loss-minimization robust method (MLR) and the simplified model-based loss-minimization robust method (SMLR). Both techniques belong to the broader category of model-based methods, which are known for their fast dynamic convergence, low torque ripple, and reduced computational load compared to search-based and hybrid methods. Results reveal that both MLR and SMLR exhibit appropriate performance against motor parameter variations, particularly in the components of stator inductance ( $L_{dq}$ ), while maintaining high efficiency and minimal power loss. The study paves the way for the integration of these techniques into control strategies that use active flux to further enhance the efficiency of SynRM in industrial applications. The paper presents a comprehensive and thorough sensitivity analysis, along with detailed experimental results, that effectively verify the proper and consistent performance of the proposed methods across various operating points.

Reducing the common mode voltage (CMV) fluctuations is crucial in transformer-less (T-less) converters. The modulation modification-based methods inherently increase the steady-state error of the compared currents due to the reduced number of voltage vectors. This error can significantly raise the total harmonic distortion (THD) output current of the inverter. This research presents a strategy of odd virtual vectors based on model-free predictive control using the extended state observer (ESO) to fix the CMV fluctuations and a significant decrease in the THD of the output current. This means the number of CMV stabilizing vectors increases with the linear combination of odd voltage vectors. The proposed method has two advantages over CMV fluctuation reduction schemes that are modulation modification-based: simultaneous control of CMV stabilization and THD reduction in T-less converters, and independence of the controller from system variables and parameters, making it a robust predictive control method. The practical results show that the proposed method, in addition to the complete CMV stabilization and the reduction of the current THD, is completely robust to the changes in the parameters of the ultralocal model and ESO compared to the model-based solutions.

Multilevel inverters (MLIs) have lately become important due to their extended application to electrical transmission and distribution systems. At the same time, the control and modulation of MLIs are especially challenging due to the high number of switching states, many of them redundant in terms of output voltage generation, and their nonlinear characteristics. In order to ease their implementation in real environment, model predictive control (MPC) is often considered, where the main control targets are: 1) to generate a the desired output current and 2) to keep the internal converter capacitor voltages at their reference value. However, a major issue with the implementation of MPC in MLIs is that the number of calculations to be done online increases dramatically with the number of levels, making it almost impossible to apply MPC in some practical cases. For these reasons, one of the main research trend in MPC for MLIs is to provide an algorithm which can reduce the computational burden necessary to operate the control. The article proposes a review of such control techniques. Starting from the basic MPC implementation and using a flying capacitor converter as an example the article review the basic strategies to avoid calculating the weighting factor in the cost function, simplifying the implementation. Also, methods to reduce the number of calculations necessary to implement MPC are shown and applied to cascaded H-bridge converters. These techniques allow to keep an high load current quality while reducing more than 95% in the number of calculations necessary to implement the control. Finally, other operation improvements of MPC are also included, such as fixed switching frequency operation and multistep MPC, reaching an important performance improvement compared to the basic MPC strategy.

This article presents the use of model predictive control (MPC) in multilevel inverters for some applications, such as, first, wind generation and, second, photovoltaics, showing that the particular restrictions of each of them can be very easily included in the control algorithm, which is an important advantage of this technique. Another application is in modular multilevel cascaded converters, where it is demonstrated that MPC can operate with very few calculations and fixed switching frequency. The second part of this article is dedicated to comparing MPC with linear control and pulsewidth modulation for multilevel inverters. The main comparison criteria are the switching losses, the distortion in the load current, and the number of commutations. The main conclusion is that MPC is a competitive alternative to linear control for application in multilevel inverters.

Conventional methodologies such as Incremental Conductance (IC) and Perturbation and Observation (P&O) can be considered effective and low-cost solutions for PV Maximum Power Point Tracking (MPPT) problems in most cases. However, these methods fail to guarantee global maximum tracking in certain situations, such as multiple peak challenges caused by Partial Shading Conditions (PSCs). Therefore, metaheuristic algorithms, like Particle Swarm Optimization (PSO), are employed in literature to address the MPPT problems during PSCs. Nevertheless, traditional PSO encounters issues such as slow convergence and a high probability of failure in tracking the global maximum power point during complex PSCs, which cause a reduction of system efficiency. To address these issues, a modified PSO hybrid with a finite control set Model Predictive Control (MPSO-MPC) has been developed as a robust MPPT technique. The MPC is incorporated into the proposed method to increase the tracking speed. The proposed approach combines a new initialization scheme that ensures uniform initial population distribution across the P-I curve. Additionally, an innovative method is used to update the search space once the partial shading pattern is detected to include only the feasible solutions in the search process. Finally, Incremental Conductance (IC) is introduced to refine the tracking process of global peak and increase its efficiency. The proposed MPSO-MPC algorithm is implemented using dSPACE MicroLabBox for real-time applications. Comprehensive investigations through MATLAB/Simulink simulation and experimental studies validate that the developed method outperforms traditional PSO and Cuckoo Search (CS) algorithms, with a convergence time that does not exceed 0.35 s and a tracking efficiency above 99.5 % under various complex PSCs. Furthermore, the results demonstrate that the proposed technique outperforms both PSO and CS across a range of environmental conditions and load disturbances.

In hazardous environments, where conditions present risks for humans, the maintenance and interventions are often done with teleoperated remote systems or mobile robotic manipulators to avoid human exposure to dangers. The increasing need for safe and efficient teleoperation requires advanced environmental awareness and collision avoidance. The up-to-date screen-based 2D or 3D interfaces do not fully allow the operator to immerse in the controlled scenario. This problem can be addressed with the emerging Mixed Reality (MR) technologies with Head-Mounted Devices (HMDs) that offer stereoscopic immersion and interaction with virtual objects. Such human-robot interfaces have not yet been demonstrated in telerobotic interventions in particle physics accelerators. Moreover, the operations often require a few experts to collaborate, which increases the system complexity and requires sharing an Augmented Reality (AR) workspace. The multi-user mobile telerobotics in hazardous environments with shared control in the AR has not yet been approached in the state-of-the-art. In this work, the developed MR human-robot interface using the AR HMD is presented. The interface adapts to the constrained wireless networks in particle accelerator facilities and provides reliable high-precision interaction and specialized visualization. The multimodal operation uses hands, eyes and user motion tracking, and voice recognition for control, as well as offers video, 3D point cloud and audio feedback from the robot. Multiple experts can collaborate in the AR workspace locally or remotely, and share or monitor the robot’s control. Ten operators tested the interface in intervention scenarios in the European Organization for Nuclear Research (CERN) with complete network characterization and measurements to conclude if operational requirements were met and if the network architecture could support single and multi-user communication load. The interface system has proved to be operationally ready at the Technical Readiness Level (TRL) 8 and was validated through successful demonstration in single and multi-user missions. Some system limitations and further work areas were identified, such as optimizing the network architecture for multi-user scenarios or high-level interface actions applying automatic interaction strategies depending on network conditions.

Differential inverters develop the PWM of internal DC-DC modules by correlating fundamental frequency of load with high switching frequency to maintain inherent characteristic operation and achieve pure higher/lower AC output voltage with smaller size. However, no generalized design method applicable for many DC-DC modules topologies has been demonstrated up till now, to realize efficient differential inverter with accurate power loss prediction. Consequently, a generic design process utilizing one parameter (modulation index gain) as well as new power losses modeling for all elements, such as switching devices, inductors, capacitors, and extra, are proposed in this paper. Novel decoupling between both operational frequencies through a two-stage calculation algorithm is the main attractive feature where, all losses are calculated based on the switching frequency of DC-DC modules then all losses are averaged accordingly over fundamental frequency. Doing this obtains RMS currents in terms of actual duty cycle and other parameters without adding complex mathematics at various PWM schemes, such as SVMS, CMS and DMS. The proposed work is simultaneously employed for many DC-DC modules topologies and verified in simulation and experiments using 0.7kW 50kHz isolated SEPIC-based DC-DC modules, as a case study. The error into actual duty cycle is decreased from $\mathrm {5.19\% }$ to $\mathrm {2.47\% }$ in the case of CMS at different power ratings. Further, it is reduced to $\mathrm {2.64\% }$ and $\mathrm {1.86\% }$ for SVMS and DMS, respectively. Finally, smart foretelling of power loss in each element of differential inverter is considered crucial for future efficiency improvement.

The paper suggests a 9-level inverter with quadruple boosting which it considers very useful for small-scale PV systems. The suggested circuit uses capacitors that are a self-balanced voltage without using an additional circuit or complicated control algorithms to balance the voltage of the capacitors. The circuit produces 9 levels with quadruple boost gain at the output terminals by using minimum components as compared with other recent topologies. The circuit analysis and modeling for each output level are described. The switching control strategy uses a simplistic logic control circuit relying on Pulse Width Modulation (PWM) is implemented to ensure balance voltage of capacitors. By the help of MATLAB/SIMULINK, the effectiveness and robustness of architecture are validated. Furthermore, a prototype for this topology is set up and tested in the laboratory using a DSPACE-1104 verifying the theoretical analysis. The experimental setup and hardware implementation are described. The theoretical and experimental results are investigated under various conditions illustrating excellent agreement between the theoretical and experimental endorses the targeted circuit.

Single-phase switched-capacitor multilevel inverters are an excellent alternative for low-power applications as they provide high voltage gain. However, their control and modulation are not simple due to the particularity of these topologies. Finite Control-Set Model Predictive Control (FCS-MPC) has therefore emerged as an attractive control alternative for these inverters. This article seeks to establish guidelines for the implementation of FCS-MPC in this type of inverter. To that end, FCS-MPC is implemented in two topologies that have previously been described in the literature as well as in a new topology. The results establish excellent methodological guidelines for applying FCS-MPC in this family of inverters, that achieve high dynamic responses.

Multilevel converters (MLCs) are widely recognized for their exceptional benefits and have emerged as the preferred choice for medium- and high-power/voltage applications. Their usage has also been extended to low-power applications to overcome issues associated with high switching frequencies and electromagnetic interference (EMI) commonly encountered in two-level converters. Common dc-link MLCs have received particular attention in industry due to their ability to eliminate the need for bulky and inefficient transformers and rectifiers, making them a compelling option for different applications, primarily medium- and high-power/voltage applications such as wind turbine (WT) power conversion systems. Furthermore, common dc-link topologies are required for back-to-back (BTB) configurations, as they facilitate the use of a shared dc-link between the rectification and inversion stages. Despite their popularity, there is currently no comprehensive review article dedicated to common dc-link topologies. This article addresses this gap by presenting a comprehensive review of common dc-link MLCs, covering their topological evolution, features, topologies comparison, modulation techniques, control strategies, and industrial application areas. Additionally, future perspectives and recommendations are discussed to provide researchers and engineers with a better understanding of the potential applications and advantages of these converters.

Tuning the weighting factor is crucial to model predictive torque and flux control. A finite set of discrete weighting factors is utilized in this research to determine the optimum solution. The Pareto line optimization technique is implemented to prevent the occurrence of local optimum solutions. By conducting an accuracy analysis, the number of discrete weighting factors is optimized, and the number of iterations is reduced. The stator current distortion minimization criterion is used to obtain the ultimate global optimal solution from the Pareto line. This study compares the results of the proposed optimization method and the particle swarm optimization method based on experimental data from a 4 kW induction motor drive test bench. The proposed technique can achieve the global optimum weighting factor in a shorter computational duration while maintaining a slightly lower total harmonics distortion and torque ripple.

Asymmetric multilevel inverters generate high-quality output voltage using the same number of components as symmetric multilevel inverters. The main drawback of these topologies is that they require many DC voltage sources, and the power switches must endure high voltage stress. In this paper, a switched-capacitor sub-module inverter topology is proposed to reduce the number of DC voltage sources and the voltage stress on the switches of asymmetric multilevel inverters. The proposed sub-module inverter can generate 15 voltage levels by using two DC power supplies and a capacitor. The voltage of the capacitor can be automatically charged at half of the input DC power supply without the need for any sensors. In addition, the capacitor charging operation does not produce an inrush current because it is charged by the direction of the output current; this is an advantage over switched capacitor multilevel inverters. A modular topology is also presented based on the proposed sub-module inverter to achieve high voltage levels while reducing the number of elements. A comprehensive comparison between the proposal and other multilevel inverter topologies is performed to validate the design of the proposed inverter. In addition, thermal and loss distribution simulations of the proposed sub-module inverter are performed. Finally, the performance, efficiency, and accuracy of the proposed inverter are confirmed through laboratory prototyping.

In this paper, a model-based maximum power point tracking (MPPT) technique is presented for a two-stage grid-connected photovoltaic (PV) system, where the loci of the maximum power points (MPPs) is specified accurately based on a new formulation. In this formulation, the effect of both irradiance and temperature is taken into consideration, whereas the irradiance is estimated to reduce the cost of the system and enhance its reliability. Furthermore, constant power generation (CPG) is integrated with the developed MPPT method to facilitate other power regulation schemes in the PV system. The proposed methodology is compared with the well-known perturb and observe (P&O) method for evaluation. Additionally, a modified version of the P&O is included in the comparison for better assessment. The effect of different partial shading conditions on the system’s performance is also investigated. The DC-link PI controller is replaced with a new adaptive DC-link controller to enhance the transient behavior of the PV system. Moreover, the suggested DC-link controller removes the DC offsets, which appear in case of gradient increase or decrease in the input PV power. In contrast to the conventional PI controller, which has poor performance at such circumstances. The active and reactive power exchange with the grid is managed using a computationally efficient finite-set model predictive control (FS-MPC) algorithm. Furthermore, switching frequency minimization is added as a secondary objective using a weighting factorless procedure. The grid-voltage sensors are eliminated and estimated using an extended Kalman filter (EKF). The overall control strategy is evaluated using experimental implementation, hardware-in-the-loop (HIL), and matlab/simulink.

Optimizing energy consumption in buildings is a significant challenge in today’s society. A major part of energy consumption is in heating, ventilation and air conditioning (HVAC) systems. In this paper, the aim is to reduce the energy consumption of air handling units (AHU) by applying optimal control. This system used in this study has four AHUs, all of which are assumed to be the same. Due to the uncertainty of the temperature of the heat exchanger’s (H/E) inlet and outlet water, a model of the system was first made using its hypothetical capacity according to the ASHRAE standards. The inlet and outlet water temperatures are calculated using simulated and real data. In order to increase the model’s accuracy and facilitate implementation on a real system, the data obtained is used to train a dynamic recurrent neural network (RNN) for the H/E. Furthermore, to increase the system’s stability and bolster its response to disturbances, which change system parameters over time and reduce the accuracy of neural network models, an online recursive least squares (RLS-based) adaptive constrained generalized predictive controller (AGPC) is used to control its outlet air temperature. The AGPC attempts to minimize the computational load and estimates the transfer function by using continuously updated input-output data from the model; this model has fewer parameters than the RNN model. Finally, the power consumption of the H/E is calculated. The outlet humidity and airflow are controlled using an optimal controller to minimize energy consumption. The results show a reduction in the energy consumption of 54.95% with respect to the previous work and of 69.9% compared to the dataset from the real system.

The main purpose of this paper is to analyze a five-phase Voltage Source Inverter (VSI) that is operated with the Pulse Width Modulation (PWM) switching technique. Double Fourier integral (DFI) analysis has been used to extract the harmonics of the line-to-line voltages. Moreover, the harmonic current ripple has been calculated for an effective inductive five-phase load with a regular pentagon connection. Correspondingly, a new closed-form solution for calculating the harmonic losses in five-phase VSIs has been derived. In addition, a new equation for the weighted total harmonic distortion (WTHD) index has been extracted for five-phase VSIs. To validate the suggested analytical solutions (i.e., harmonic losses and WTHD equations), the results are compared with the DFI calculation method and the method of applying the fast Fourier transform (FFT) to a simulated waveform. The results show that the proposed analytical method has high accuracy and requires less mathematical effort, especially at high-frequency ratio values. Finally, the study includes simulation results and the implementation of an experimental setup.

This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications. A relevant feature of the proposed solver is that the total number of operations can be computed in the worst-case scenario. Thus, the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. The proposed method outperforms general-purpose algorithms in terms of computation time, while keeping the same accuracy.

Robotic interventions with redundant mobile manipulators pose a challenge for telerobotics in hazardous environments, such as underwater, underground, nuclear facilities, particle accelerators, aerial or space. Communication issues can lead to critical consequences, such as imprecise manipulation resulting in collisions, breakdowns and mission failures. The research presented in this paper was driven by the needs of a real robotic intervention scenario in the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN). The goal of the work was to develop a framework for network optimisation in order to help facilitate Mixed Reality techniques such as 3D collision detection and avoidance, trajectories planning, real-time control, and automatized target approach. The teleoperator was provided with immersive interactions while preserving precise positioning of the robot. These techniques had to be adapted to delays, bandwidth limitation and their volatility in the 4G shared network of the real underground particle accelerator environment. The novel application-layer congestion control with automatic settings was applied for video and point cloud feedback. Twelve automatic setting modes were proposed with algorithms based on the camera frame rate, resolution, point cloud subsampling, network round-trip time and throughput to bandwidth ratio. Each mode was thoroughly characterized to present its specific use-case scenarios and the improvements it brings to the adaptive camera feedback control in teleoperation. Finally, the framework was presented according to which designers can optimize their Human-Robot Interfaces and sensor feedback depending on the network characteristics and task.