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Abstract The application of Model-Free Predictive Control (MFPC) in power electronic systems has garnered increasing attention. In this paper, MFPC control based on replacing the classical factory model with an ultra-local model (ULM) is studied. Generally, the Integral Sliding Mode Observer (ISMO) is used to estimate the unknown part in the ULM where the non-physical factor in the ULM is selected with approximate values ​​ranging from the nominal value of the system which is contrary to the concept of MFPC control. In this research, an improved adaptive integral sliding mode observer (AISMO) based MFPC (AISMO-MFPC ) is proposed to estimate this factor with the unknown function in the ULM equation. The new observer design enables the estimation of this factor based on the current error, which allows for independent prposedcontrol of the system parameters.To obtain the lowest current ripple, the concept of active vector execution time (AVET ) has been incorporated into the proposed control where two vectors are selected in the sampling period to minimize the cost function instead of selecting a single vector. ULM is also used to calculate AVET which facilitates the implementation of the imposed control. The combination of the proposed AISMO-MFPC and AVET gives faster system response and reduces the current ripple and lower harmonics, especially in case of mismatch parameters. Finally, the effectiveness of the proposed control is confirmed under various conditions by the presented simulation and experimental results.

This paper proposes a new diagnostic approach for identifying bearing faults in induction motors, which are common issues that can affect the performance and durability of these motors. Although various methods have been developed to diagnose these faults, we propose a high-resolution technique based on stator current analysis, enabling more effective detection and identification of these anomalies. This new approach leverages a variant of the Root-Prony method, designed to overcome the limitations of conventional periodogram methods, such as the inability to achieve high-frequency resolution with very short acquisition times. These limitations make it challenging to analyze the frequency components associated with bearing faults. In contrast, high-resolution spectral analysis using the Root-Prony method offers enhanced frequency resolution and the ability to detect faults even at low amplitudes. However, the method's high computation time remains a limiting factor. To address this, a zoomed Root-Prony method is proposed, allowing for faster and more precise diagnosis of bearing faults. Simulation and experimental tests are presented in this paper to validate the effectiveness of the proposed method.

Due to the growing global demand for electricity energy, photovoltaic systems are becoming increasingly important as a continuous and environmentally friendly alternative. They ensure the continuity of electrical production in a healthy and sustainable manner. To ensure the efficiency and optimal performance of these systems, an effective diagnostic model is urgently needed to classify faulty and working solar cells. In recent years, deep learning methods have been used to analyse and process images, providing new insights and guidance in the field of fault diagnosis in PV systems. This research proposes a comparative study of the deep learning models ResNet50, VGG-19, and AlexNet to test their effectiveness in analysing and classifying defective solar cells from non-defective cells using EL images.

Abstract Under the presence of nonlinear load, the most existing virtual impedance (VI) methods-based control solution performs poorly in reactive power sharing among droop-operated VSIs in microgrids (MGs). This may be due to the involved estimation techniques for extracting the current harmonics at selected frequencies, which suffer from either poor accuracy of the harmonic estimation and/or the effect of DC offset in the measurements. Such an issue may affect the performance of the virtual impedance control, hence, the system stability. To bridge this gap, the implementation of the virtual impedance based on multiple enhanced second-order generalized integrator (MESOGI) suitable for harmonics and DC-offset estimation/rejection, is proposed in this paper. The MESOGI can offer an accurate estimation of the current quadrature components free from DC offset at selected frequencies, required to implement the virtual impedance control. Therefore, it makes the designed virtual impedance-based control scheme robust to voltage distortions, immune to DC disturbance, and capable of sharing properly the power harmonics. As a result, this may contribute to improving the reactive and harmonic power-sharing between droop-controlled VSIs within an islanded MG. The modeling of the MESOGI scheme and its performance investigation is carried out. In addition, the mathematical model of the implemented virtual impedance is derived. Further, analysis based on the obtained model of the equivalent output impedance including virtual impedance is established to study its effect. Simulation and experimental tests are performed to prove the effectiveness of the control proposal in improving the reactive power sharing under nonlinear load operating conditions.

In the absence of portable charging devices, sectors such as transportation, communication, and emergency services deal with various challenges towards electric power needs while compromising on (1) operational efficiency, (2) insufficient portable charging solutions, and (3) limited versatility. This highlights the critical need for reliable and multi-functional power solutions. To provide a portable charging solution across diverse sectors, this paper proposes an innovative development of a solar-powered multi-functional portable charging device (SPMFPCD) with internet- of-thing (IoT)-based monitoring capabilities. The proposed scheme introduces a comprehensive model integrating advanced technologies which include a highly efficient solar panel, charge controller, sensors, and IoT module. The proposed system facilitates versatile charging solutions for a wide range of power requirements with real-time monitoring and data analysis through the IoT platform. Moreover, the proposed work explores the applications of the SPMFPCD in (1) emergency medical scenarios, (2) outdoor adventures, (3) disaster management, and 4) public spaces. Performance evaluation was made by proposing case studies to validate the (1) economic viability, (2) power management, and (3) environmental impact of widespread deployment of SPMPFCD in public spaces. Furthermore, detailed analysis of battery energy storage system (BESS) and photovoltaic (PV) integration for load management, seasonal dynamics, and renewable energy integration (REI) contribute to a comprehensive understanding of the proposed solution.

Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.

This study introduces a novel approach to enhance state and unknown input estimation for the synchronous reluctance motor, utilizing the Takagi-Sugeno fuzzy representation. A proportional multi-integral observer structure is introduced, capable of capturing a wider range of unknown input dynamics compared to the previous proportional integral observer utilized in prior research. Stability conditions, obtained using the quadratic Lyapunov function, are formulated as linear matrix inequalities to guarantee the asymptotic convergence of the estimation error. To evaluate the effectiveness of the proposed observer, different cases were considered, including scenarios with slow and fast variations of the unknown input. Through hardware-in-the-loop validation, the results clearly demonstrate the superiority of the proposed method over the earlier approach. This advancement signifies a significant improvement in the estimation accuracy and reliability of the Synchronous Reluctance Motor.

Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of automated systems for efficient monitoring. The study presented in this paper focuses on detecting biofouling on tidal stream turbine blades using camera-based monitoring. The process begins with dividing the video into a series of images, which are then annotated to identify and select the bounding boxes containing objects to be detected. These annotated images are used to train YOLO version 8 to detect biofouled and clean blades in the images. The proposed approach is evaluated using metrics that demonstrate the superiority of this YOLO version compared to previous ones. To address the issue of misdetection, a data augmentation approach is proposed and tested across different YOLO versions, showing its effectiveness in improving detection quality and robustness.

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

Drones are a promising technology performing various functions, ranging from aerial photography to emergency response, requiring swift fault diagnosis methods to sustain operational continuity and minimise downtime. This optimises resources, reduces maintenance costs, and boosts mission success rates. Among these methods, traditional approaches such as visual inspection or manual testing have long been utilised. However, in recent years, data representation methods, such as deep learning systems, have achieved significant success. These methods learn patterns and relationships, enhancing fault diagnosis, but also face challenges with data complexity, uncertainties, and modelling complexities. This paper tackles these specific challenges by introducing an efficient representation learning method denoted Multiverse Augmented Recurrent Expansion (MVA-REX), allowing for an iterative understanding of both learning representations and model behaviours and gaining a better understanding of data dependencies. Additionally, this approach involves Uncertainty Bayesian Optimisation (UBO) under Extreme Learning Machine (ELM), a lighter neural network training tool, to tackle both uncertainties in data and reduce modelling complexities. Three main realistic datasets recorded based on acoustic emissions are involved in tackling propeller and motor failures in drones under realistic conditions. The UBO-MVA Extreme REX (UBO-MVA-EREX) is evaluated under many, error metrics, confusion matrix metrics, computational cost metrics, and uncertainty quantification based on both confidence and prediction interval features. Application compared to the well-known long-short term memory (LSTM), under Bayesian optimisation of the approximation error, demonstrates performances, certainty, and cost efficiency of the proposed scheme. More specifically, the accuracy obtained by UBO-MVA-EREX, ~0.9960, exceeds the accuracy of LSTM, ~0.9158, by ~8.75%. Besides, the search time for UBO-MVA-EREX is ~0.0912 s, which is ~98.15% faster than LSTM, ~4.9287 s, making it highly applicable for such challenging tasks of fault diagnosis-based acoustic emission signals of drones.

Abstract A coordinated scheduling model based on two-stage distributionally robust optimization (TSDRO) is proposed for integrated energy systems (IESs) with electricity-hydrogen hybrid energy storage. The scheduling problem of the IES is divided into two stages in the TSDRO-based coordinated scheduling model. The first stage addresses the day-ahead optimal scheduling problem of the IES under deterministic forecasting information, while the second stage uses a distributionally robust optimization method to determine the intraday rescheduling problem under high-order uncertainties, building upon the results of the first stage. The scheduling model also considers collaboration among the electricity, thermal, and gas networks, focusing on economic operation and carbon emissions. The flexibility of these networks and the energy gradient utilization of hydrogen units during operation are also incorporated into the model. To improve computational efficiency, the nonlinear formulations in the TSDRO-based coordinated scheduling model are properly linearized to obtain a Mixed-Integer Linear Programming model. The Column-Constraint Generation (C&CG) algorithm is then employed to decompose the scheduling model into a master problem and subproblems. Through the iterative solution of the master problem and subproblems, an efficient analysis of the coordinated scheduling model is achieved. Finally, the effectiveness of the proposed TSDRO-based coordinated scheduling model is verified through case studies. The simulation results demonstrate that the proposed TSDRO-based coordinated scheduling model can effectively accomplish the optimal scheduling task while considering the uncertainty and flexibility of the system. Compared with traditional methods, the proposed TSDRO-based coordinated scheduling model can better balance conservativeness and robustness.

This paper presents PrognosEase; a software that provides an easier way to produce different types of run-to-failure data mimicking real-world conditions to simplify prognosis studies in terms of data collection and improvement in machine learning degradation modeling process. Different types of degradation types made available to meet different types of applications. Besides, some primary machine learning experiments were performed to ensure that complexity patterns of real systems could be observed in the training/testing predictions attitude. This paper also presents the impacts, limitations and potential improvements of the data generator.

This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times.

In recent years, the growing demand for efficient and sustainable energy management has led to the development of innovative solutions for embedded systems. One such solution is the integration of hybrid nanogrid energy management systems into various applications. There are currently many energy management systems in different domains, such as buildings, electric vehicles, or even naval transport. However, an embedded nanogrid management system is subject to several constraints that are not sufficiently studied in the literature. Indeed, such a system often has a limited energy reserve and is isolated from any energy supply for a long time. This paper aims to provide a comprehensive overview of the current state of research, advancements, and challenges in the field of hybrid nanogrid energy management systems. Furthermore, it offers a comparative analysis between hybrid nanogrids and microgrids and the implications of their integration in embedded systems. This paper also discusses the key components, operation principles, optimization strategies, real-world implementations, challenges, and future prospects of hybrid nanogrid energy management systems. Moreover, it highlights the significance of such systems in enhancing energy efficiency, reducing carbon footprints, and ensuring reliable power supply.

Motivated by the significant efforts developed by researchers and engineers to improve the economic and technical performance of microgrids (MGs), this paper proposes an Active Disturbance Rejection Control (ADRC) for Distributed Energy Resources (DER) in microgrids. This approach is a nonlinear control that is based on a real-time compensation of different estimated disturbances. The DER operates along with the electrical grid to provide the load requirements. This load has a nonlinear and uncertain character, which presents a source of unmodeled dynamics and harmonic perturbations of the MG. The main objective of this paper is to ensure the stability and the continuity of service of the distributed generation resources by controlling the DC-AC converter. The ADRC as a robust control technique is characterized by its ability to compensate for the estimated total disturbances caused by the load variation and the external unmodeled perturbations to guarantee the high tracking performance of sinusoidal reference signals in the DER system. The ADRC technique is characterized by its nonlinear function, which provides a high robustness to the controlled system. However, in order to simplify the control structure by keeping its high reliability, this paper proposes to replace the nonlinear function with a simple error (termed linear ADRC), compares the impact of this modification on the system performances, and evaluates its operation in the presence of linear and nonlinear load variations. Simulation results are presented to demonstrate the efficiency of the proposed control approach for a three-phase DER.

The deep learning diagnosis of aircraft engine-bearing faults enables cost-effective predictive maintenance while playing an important role in increasing the safety, reliability, and efficiency of aircraft operations. Because of highly dynamic and harsh operating conditions of this system, such modeling is challenging due to data complexity and drift, making it difficult to reveal failure patterns. As a result, the objective of this study is dual. To begin, a highly structured data preprocessing strategy ranging from extraction, denoising, outlier removal, scaling, and balancing is provided to solve data complexity that resides specifically in outliers, noise, and data imbalance problems. Gap statistics under k-means clustering are used to evaluate preprocessing results, providing a quantitative estimate of the ideal number of clusters and thereby enhancing data representations. This is the first time, to the best of authors’ knowledge, that such a criterion has been employed for an important step in a preliminary ground truth validation in supervised learning. Furthermore, to tackle data drift issues, long-short term memory (LSTM) adaptive learning features are used and subjected to a learning parameter improvement method utilizing recursive weights initialization (RWI) across several rounds. The strength of such methodology can be seen by application to realistic, extremely new, complex, and dynamic data collected from a real test-bench. Cross validation of a single LSTM layer model with only 10 neurons shows its ability to enhance classification performance by 7.7508% over state-of-the-art results, obtaining a classification accuracy of 92.03 ± 0.0849%, which is an exceptional performance in such a benchmark.

Deep Learning (DL) for monitoring slowly evolving degradation processes typically involves overcoming data drift, complexity, and unavailability issues resulting from dynamic and harsh conditions and the rarity of labeled failure patterns, respectively. While degradation patterns are mostly hidden in such complex data, observation-based DL is prone to producing uncertain predictions and/or overfit models during the training process. This problem is usually caused by the insignificance of certain data representations. Therefore, and particularly due to the sequential nature of data in such a degradation process, it is necessary to consider neighboring observations to judge the accuracy of a representation or improve it. In this context, instead of providing traditional observation-based learning philosophy, this paper presents data-driven sequential mapping while additionally showing that health indices can also be represented as a vector of sequential data and not as a single regressor output changing a model’s architecture. Using a dataset generated from a mathematical model mimicking bearing degradation life cycles and responding to the aforementioned three main challenges, a comparative study built on investigating observation-based and sequence-based learning paths was conducted. According to a well-defined visual and numerical evaluation criterion, a sequence-based methodology reflects a better understanding of data representations through parameter tuning, achieving better approximation and generalization. Such results support the necessity of such a learning mechanism, especially for sequential data, dealing with some sort of correlation and degrading controversy. The files required to reproduce the findings of this work have been made publicly available.

Prognostic studies of industrial systems essentially focus on health deterioration analysis that has recently been oriented toward data analytics and learning systems. In general, real degradation phenomena suffer from complex drifted data in which degradation patterns are hidden and change over time. Accordingly, such a process requires a well-structured processing and extraction mechanism to reveal such patterns, which facilitates the transition to other model reconstruction and investigation tasks. In this context, to provide additional simplicity of data processing in the field, a complete software package is designed and grouped into a single function that is fully automated and does not require human intervention. The package named ProgMachina (i.e., prognostic machine) provides a featured list of processed features from a life cycle that passed through denoising, filtering, outlier removal, and scaling process to ensure data significance in terms of degradation. The package allows for the use of a time window with a specific overlap to ensure that the scanning process of all possible degradation patterns is properly done. Additionally, an exponential function is used to identify a corresponding health index of degraded signals. In addition, a set of well-known metrics is used to assess the degradation of extracted features. Data visualization and many previous experiments on machines show the effectiveness of such a methodology in terms of obtained prediction accuracy and degradation assessment. The package is designed with Matlab software and made available online to be exploited in similar fields.

Renewable energy generation is increasingly important due to serious energy issues. A Doubly Salient Permanent Magnet Generator (DSPMG) can be an interesting candidate for tidal stream renewable energy systems. However, the special structure makes the system nonlinear and strongly coupled even after Park transformation and involves a larger torque ripple. Previous research mainly focused on model-based control for this machine, which is very sensitive to the parameters. Thus, to control the complex systems stably and accurately, two model-free control algorithms, Active Disturbance Rejection-Based Iterative Learning Control (ADRILC) and Active Disturbance Rejection Control–Iterative Learning Control (ADRC–ILC), are proposed for the current and speed control loops of a DSPMG-based Tidal Stream Turbine (TST), respectively. ADRC–ILC uses ADRC to deal with the external non-periodic speed ripple and adopts ILC to reduce the internal periodic speed ripple. ADRILC employs an iterative method to improve the ESO for the enhancement of the convergence rate of ILC. Considering the variable tidal speed, when the speed is above the rated value, Maximum Power Point Tracking (MPPT) must be changed to a power limitation strategy for limiting the generator power to the rated value and extending the system operating range. Thus, Optimal Tip Speed Ratio (OTSR)-based MPPT (for a low tidal current speed) and Leading Angle Flux-Weakening Control (LAFWC) (for a high tidal current speed) strategies are also proposed. According to the simulation results, the proposed ADRC–ILC + ADRILC has the lowest torque ripple, the highest control accuracy, as well as a good current tracking capability and strong robustness. At the rated speed, the proposed method reduces the torque ripple by more than 20% and the speed error by about 80% compared with PI control: the current difference is limited in 2A. The LAFWC proposed for an excessive tidal current speed is effective in conserving the electromagnetic power and increasing the generator speed.

In this paper, a new modified model predictive control is proposed to improve the performance of the model predictive control for two-level voltage source inverters by alleviating computational burden and the disadvantages associated with the conventional model predictive control strategy. The objective of the proposed method is to reduce the number of candidate voltage vectors in each sector, thereby improving the overall performance of the control system, as well as achieving common-mode voltage reduction for two-level voltage source inverters. Two strategies are introduced to achieve this objective. Firstly, an algorithm is developed based on statistical computational processes to pre-define the candidate voltage vectors. This strategy involves ranking and considering the most frequently used voltage vectors. Consequently, by reducing the computational burden, the search space for the optimal voltage vectors is reduced. Furthermore, based on statistical results, a strategy is proposed to divide the sectors into three sectors instead of the six sectors in the conventional method. This approach effectively reduces the number of candidate voltage vectors. The modified model predictive control strategy aims to improve the efficiency of the control system by reducing the computational burden, and suppressing the common-mode voltage. The simulation and experiments are carried out to verify the effectiveness of the proposed strategy under various operational conditions. The results demonstrate that the modified model predictive control approach significantly reduces the computational burden and complexity of the control system while effectively suppressing the common-mode voltage; this contributes to improving the performance of two-level voltage source inverters and enhancing their applicability for connecting the renewable energies to the grid.

The diagnosis and prognosis of aeronautical-bearing health conditions are essential to proactively ensuring efficient power transmission, safety, and reduced downtime. The rarity of failures in such safety-critical systems drives this process towards data-driven analytics of fault injection and aging experiments, rather than complex physics-based modeling. Nonetheless, data-based condition monitoring is very challenging due to data complexity, unavailability, and drift resulting from distortions generated by harsh operating conditions, scarcity of failure patterns, and rapid data change, respectively. Accordingly, the objective of this work is three-fold. First, to reduce data complexity and improve feature space representation, a robust data engineering scheme, including feature extraction, denoising, outlier removal, filtering, smoothing, scaling, and balancing, is introduced in this work. Second, collaborative selection-based incremental deep transfer learning (CSIDTL) is introduced to overcome the problem of the lack of patterns, incrementing the number of source domains in different training rounds. Third, long short-term memory (LSTM) adaptive learning rules are fully taken into account to combat further data complexity and data change problems. The well-structured methodology is applied on a huge dataset of aeronautical bearings dedicated to both diagnostic and prognosis studies, which perfectly addresses the above challenges in a form of a classification problem with 13 different conditions, 7 operating modes, and 3 stages of damage severity. Conducting CSIDTL following a three-fold cross-validation process allows us to improve classification performance by about 12.15% and 10.87% compared with state-of-the-art methods, reaching classification accuracy rates of 93.63% and 95.65% in diagnosis and prognosis, respectively.

Resistance to differential cryptanalysis is a fundamental security requirement for symmetric block ciphers, and recently, deep learning has attracted the interest of cryptography experts, particularly in the field of block cipher cryptanalysis, where the bulk of these studies are differential distinguisher based black-box attacks. This paper provides a deep learning-based decryptor for investigating the permutation primitives used in multimedia block cipher encryption algorithms.We aim to investigate how deep learning can be used to improve on previous classical works by employing ciphertext pair aspects to maximize information extraction with low-data constraints by using convolution neural network features to discover the correlation among permutable atoms to extract the plaintext from the ciphered text without any P-box expertise. The evaluation of testing methods has been conceptualized as a regression task in which neural networks are supervised using a variety of parameters such as variations between input and output, number of iterations, and P-box generation patterns. On the other hand, the transfer learning skills demonstrated in this study indicate that discovering suitable testing models from the ground is also achievable using our model with optimum prior cryptographic expertise, where we contribute the results of deep learning in the field of deep learning based differential cryptanalysis development.Various experiments were performed on discrete and continuous chaotic and non-chaotic permutation patterns, and the best-performing model had an MSE of $1.8217{e}^{-04}$ and an $R^{2}$ of 1, demonstrating the practicality of the suggested technique.

Abstract Development of advanced grid‐synchronization technique for unbalanced and distorted grid is considered in this paper. In this context, self‐tuning filter (STF) is considered to extract the fundamental component from the measured unbalanced and distorted voltages. Standard STF considers balanced grid voltages which is not always possible to ensure in the actual power grid. To mitigate this issue, an extended STF is proposed by analyzing the standard STF in the state‐space framework. To make the proposed ESTF grid‐following, a robust open‐loop frequency estimator is also applied. The closed‐loop system enjoys excellent filtering benefit from the extended STF side while taking advantage of the fast convergence speed property of the open‐loop frequency estimation technique. Numerical simulation and experimental results with double second‐order generalized integrator phase‐locked loop are provided to validate the theoretical developments.

The optimization of energy production in renewable energy systems is crucial to improve energy efficiency. In this context, the aim of this study focuses on maximizing the efficiency of a switched reluctance generator. This paper presents a novel approach to enhance the electrical power and efficiency of a switched reluctance generator by determining the optimal operating parameters based on the mechanical input power of the system. The proposed strategy consists of the following steps: First, an algorithm was developed that provides machine data for different power modes based on control parameters, including electrical and mechanical powers such as speed, torque, and turn-on and turn-off angles. In the next step, the obtained data were analyzed to identify the optimum points corresponding to the states with maximum power and efficiency for various scenarios. An algorithm for maximum power point tracking was also developed to determine the optimal parameters as a function of mechanical energy. Finally, the data and algorithms were integrated into the switched reluctance generator control system. Simulations were conducted to compare the proposed MPPT technique with other techniques. This comparison is essential to validate the effectiveness of the proposed strategy in achieving enhanced electrical power generation efficiency.

The fifth revolution of the industrial era—or Industry 5 [...]

Machine learning applications usually become a subject of data unavailability, complexity, and drift, resulting from massive and rapid changes in data volume, velocity, and variety (3V). Recent advances in deep learning have brought many improvements to the field, providing generative modeling, nonlinear abstractions, and adaptive learning to address these challenges. In fact, deep learning aims to learn from representations that provide a consistent abstraction of the original feature space, which makes it more meaningful and less complex. However, data complexity related to different distortions, such as higher levels of noise, remains difficult to overcome. In this context, recurrent expansion (RE) algorithms have recently been developed to explore deeper representations than ordinary deep networks, providing further improvement in feature mapping. Unlike traditional deep learning, which extracts meaningful representations through inputs abstraction, RE enables entire deep networks to be merged into another one consecutively, allowing the exploration of inputs, maps, and estimated targets (IMTs) as primary sources of learning; three sources of information that provide additional knowledge about their interactions in a deep network. In addition, RE makes it possible to study IMTs of several networks and learn significant features, improving its accuracy with each round. In this context, this paper presents a general overview of RE, its main learning rules, advantages, disadvantages, and its future opportunities, while reviewing the state-of the art and providing some illustrative examples.

In the context of harvesting tidal stream energy, which is considered a promising source of renewable energy due to its high energy density, stability, and predictability, this paper proposes a review-based roadmap investigating the use of data-driven techniques, more specifically machine learning-based approaches, to detect and estimate the extent of biofouling in tidal stream turbines. An overview of biofouling and its impact on these turbines will be provided as well as a brief review of current methodologies and techniques for detecting and estimating biofouling. Additionally, recent developments and challenges in the field will be examined, while providing several promising prospects for biofouling detection and estimation in tidal stream turbines.

Electric vehicles (EV) and photovoltaic (PV) systems are increasingly becoming environmentally friendly and more affordable solutions for consumers. This article discusses the integration of PV and EV in a residential system to meet the requirements of residential loads taking into account the PV supplied power, availability and the state of charge (SOC) of EVs. A hybrid control model has been proposed to control the residential system. The combined PI-Fuzzy logic controller is employed to control the buck-boost bi-directional converter. The DC-AC grid-side converter is controlled by the ADRC controller. The effectiveness of PI-Fuzzy logic controller in reducing voltage and current ripples and ADRC controller in rejecting disturbances is demonstrated in each case. A rule-based energy management strategy has been proposed to control the flow of energy between the components of the residential system. The suggested energy management system (EMS) covers every scenario that might occur. Whether the EV is linked to the home or not, and also takes into account the owner using the EV in an emergency situation. The EV operates in two modes, Home-to-Vehicle (H2V) mode and Vehicle-to-Home (V2H) mode, depending on the power produced by the PV and the conditions related to the EV. All possible scenarios are tested and validated. The simulation results show that the proposed EMS is a reliable solution that can reduce the power grid intervention.

Due to the harsh and changeable marine environment, one low speed stator-permanent magnet machine named doubly salient permanent magnet machine with toothed pole is applied for marine current energy conversion system. Indeed, this machine has simple structure, intriguing fault tolerance, and higher power density, which could adequately satisfy the different complicated operation conditions. However, its permanent magnet flux-linkage has the same variation period as the inductance which leads to a strong nonlinear coupling system. Moreover, the torque ripple caused by this special characteristics, uncertainty of system parameters and disturbance of load greatly increases the difficulty of control in this strongly coupling system. Consequently, the classical linear PI controller is difficult to meet the system requirement. In this paper, the high-order sliding mode control strategy based on the super-twisting algorithm for this system is creatively utilized for the first time. The stability of the system within a limited time is also proved with a quadratic Lyapunov function. The relative simulation results demonstrate convincingly that, the high-order sliding mode control has little chattering, high control accuracy and strong robustness.

Energy use in buildings is increasing to provide optimal comfort for the occupants. People spend 90% of their lifetime in buildings. Therefore, indoor environment quality and comfort management have a crucial role in maintaining occupants’ health and productivity. Reducing energy consumption for optimal comfort management is important to minimize CO2 emission and global warming by the building sector. According to the literature, it is possible to control and reduce energy consumption by monitoring occupants’ behavior and estimating the number of occupants. A critical review is carried out in this paper to analyze the existing methodologies for modeling occupant behavior and prediction with respect to comfort and energy management. A comprehensive analysis is also performed on recent developments and challenges in modeling, along with recommendations and future perspectives.

In this article, a new solar PV fed Dynamic Voltage Restorer (DVR) based on Trans-Z-source Inverter (TransZSI) is proposed to improve the power quality of on-grid Photovoltaic (PV) systems. DVR is a power electronic compensator using for injecting the desired voltage to the Point of Common Coupling (PCC) as per the voltage disturbance. In the proposed DVR, in place of traditional VSI, TransZSI with outstanding merits of buck/boost, a broader range of voltage boost gain, fewer passive components, and lower voltage stress, is put forth. For efficient detection, accurate voltage disturbances mitigation, and also lessening the injected voltage harmonics, a hybrid Unit Vector Template with Maximum Constant Boost Control (UVT-MCBC) method is proposed for TransZSI-DVR. The performance of the proposed TransZSI-DVR with UVT-MCBC has been analyzed under severe sag, slight sag with harmonics, swell, and interruption. The comparative studies and simulation results have shown the effectiveness of the proposed TransZSI-DVR, as opposed to traditional ZSI-DVR and VSI-DVR. The TransZSI-DVR in the PV system has mitigated voltage sag/swell/interruption. It has also improved the power quality of both the injected voltage to the PCC and PV system's output voltage.

Renewable Energy Systems have been in the spotlight of the academic and industrial research for more than two decades, thanks to the development of several fields related to the Electrical Engineering. More recently, with the increasing complexity of the individual renewable energy systems and the interconnection to the grid, the scientific panorama has been witnessing to a convergence of different topics, which span across several IEEE-IES thematic areas: power electronics, electrical machines, smart grids, energy storage, transportation electrification and aerospace. After a brief overview of the renewable energy technologies, this work deals with how the convergence of multiple technologies developed to provide marginal support to the grid has evolved into the foundation of the future utility grid and expanded to transportation sector. It will be shown how the design of a renewable energy system cannot prescind anymore from the electrical grid and from the ancillary services that are requested. Example of convergence are given for a smart transformer application and for a transportation application.

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.

This paper deals with a design methodology of permanent magnets (PM) generators used for fixed-pitch tidal turbines in a marine renewable energy context. In the case of underwater turbines, fixed-pitch tidal turbines could be very attractive and interesting to reduce maintenance operation by avoiding using such a complex electromechanical system for blade- pitching. In this technological case, one of the main control challenges is to ensure power limitation at high tidal current velocities. This control mode can be achieved using the generator flux-weakening. In this context, this paper proposes an original and systemic design methodology to optimize the generator design taking into account the tidal turbine power limitation for high tidal currents velocities. Keywords: Tidal turbine, Fixed-pitch turbine, Direct-drive, PM generator, Design methodology, Flux-weakening power limitation

Grid synchronization plays an important role in the grid integration of renewable energy sources. To achieve grid synchronization, accurate information of the grid voltage signal parameters are needed. Motivated by this important practical application, this paper proposes a state observer-based approach for the parameter estimation of unbalanced three-phase grid voltage signal. The proposed technique can extract the frequency of the distorted grid voltage signal and is able to quantify the grid unbalances. First, a dynamical model of the grid voltage signal is developed considering the disturbances. In the model, frequency of the grid is considered as a constant and/or slowly-varying but unknown quantity. Based on the developed dynamical model, a state observer is proposed. Then using Lyapunov function-based approach, a frequency adaptation law is proposed. The chosen frequency adaptation law guarantees the global convergence of the estimation error dynamics and as a consequence, ensures the global asymptotic convergence of the estimated parameters in the fundamental frequency case. Gain tuning of the proposed state observer is very simple and can be done using Matlab commands. Some guidelines are also provided in this regard. Matlab/Simulink based numerical simulation results and dSPACE 1104 board-based experimental results are provided. Test results demonstrate the superiority and effectiveness of the proposed approach over another state-of-the art technique.

This paper studies the phase and frequency estimation problem of single-phase grid voltage signal in the presence of DC offset and harmonics. For this purpose, a novel parameterized linear model of the grid voltage signal is considered where the unknown frequency of the grid is considered as the parameter. Based on the developed model, a linear observer (Luenberger type) is proposed. Then using Lyapunov stability theory, an estimator of the unknown grid frequency is developed. In order to deal with the grid harmonics, multiple parallel observers are then proposed. The proposed technique is inspired by other Luenberger observers already proposed in the literature. Those techniques use coordinate transformation that requires real-time matrix inverse calculation. The proposed technique avoids real-time matrix inversion by using a novel state-space model of the grid voltage signal. In comparison to similar other techniques available in the literature, no coordinate transformation is required. This significantly reduces the computational complexity w.r.t. similar other techniques. Comparative experimental results are provided with respect to two other recently proposed nonlinear techniques to show the dynamic performance improvement. Experimental results demonstrate the suitability of the proposed technique.

Grid-connected converters are an important class of power electronic systems. Many power and energy applications require grid-connected converters. To control grid-connected converters, precise information of the grid voltage frequency and phase are required. Gradient estimators can be very useful in this regard. They are suitable to estimate the frequency and phase of the grid voltage signal. Various gradient estimators are already available in the literature e.g. regression-based techniques. However, most of them are designed by using the instantaneous estimation error as the cost-function. This amplifies the effect of noise in the estimated parameters. To overcome this issue, an integral cost-function is considered in this paper. The integral cost-function tries to minimize the estimation error over the integration window leading to reduce the effect of noise in the estimated frequency and phase. Moreover, the cost-function uses tunable forgetting factor to give more importance to recent data. The proposed gradient estimator assumes the grid frequency to be constant. However, in practice the frequency is variable with known nominal value. To overcome this problem, a frequency estimation block is coupled with the gradient estimator. The frequency estimation block uses the idea of phase-based frequency estimation. Comparative numerical simulation and experimental studies are performed to demonstrate the suitability of the proposed technique over three other advanced techniques from the literature.

Blade imbalance fault caused by the marine organisms is considered as the most important fault in marine current turbines. Therefore, it is important to detect the fault accurately and quickly to mitigate its effect, minimize the downtime, and maximize the productivity. Imbalance fault detection methods using generator stator current signals have attracted attentions due to their low cost, operability and stability compared to the ones using vibration analysis. However, it is difficult to extract the fault signature and automatically detect the imbalance fault under different flow velocity conditions. In this paper, a wavelet threshold denoising-based imbalance fault detection method using the stator current is proposed. The signal is analyzed through three consecutive steps: the parameters offline setting based on wavelet threshold denoising, the Hilbert transform method and the Principle Component Analysis-based detection method. With this approach, the imbalance fault can be detected automatically. The imbalance fault detection is assessed under different flow velocity conditions and validated using an experimental platform. The results are promising with false alarm and false negative rates less than 1% and 5% respectively when using Q statistic. Moreover, the experimental results show that the proposed method has good stability under different flow velocity conditions.

Prosumer concept and digitilization offer the exciting potential of microgrid transactive energy systems at distribution level for reducing transmission losses, decreasing electric infrastructure expenditure, improving reliability, enhancing local energy use, and minimizing customers' electricity bills. Distributed energy resources, demand response, distributed ledger technologies, and local energy markets are integral parts of transaction energy system for emergence of decentralized smart grid system. Hence, this paper discusses transactive energy concept and proposes seven functional layers architecture for designing transactive energy system. The proposed architecture is compared with practical case study of Brooklyn microgrid. Moreover, this paper reviews the existing architectures and explains the widely known distributed ledger technologies (blockchain, directed acyclic graph, hashgraph, holochain, and tempo) alongwith their advantages and challenges. The local energy market concept is presented and critically analyzed for energy trade within a transactive energy system. This paper also reviews the potential and challenges of peer-to-peer and community-based energy markets. Proposed architecture and analytical review of distributed ledger technologies and local energy markets pave the way for advanced research and industrialization of transactive energy systems.

The conventional power system has been evolving towards a smart grid system for the past few decades. An integral step in successful realization of smart grid is to deploy renewable energy resources, particularly rooftop photovoltaic systems, at smart homes. With demand response opportunities in smart grid, residential customers can manage the utilization of their demand responsive appliances for getting economic benefits and incentives in return. In this regard, this paper proposes an effective home energy management system for residential customer to optimally schedule the demand responsive appliance in the presence of local photovoltaic and energy storage systems. For efficient home-to-grid energy transactions in home energy management system, the stochastic nature of photovoltaic power generation is modeled with the beta probability distribution function for solar irradiance. The main contribution of this paper is the application of polar bear optimization (PBO) method for optimally solving the scheduling problem of demand responsive appliances in home energy management system to minimize electricity consumption cost as well as peak-to-average ratio. The effectiveness of the proposed metaheuristic optimization technique is proven by performing different case studies for a residential consumer with different base load, uninterruptible deferrable, and interruptible deferrable appliances under a real-time energy price program. Comparative results with different metaheuristic techniques available in the literature show that the electricity consumption cost and peak-to-average ratio are effectively optimized using the proposed PBO algorithm.

A grid-synchronization-based fundamental frequency positive-sequence (FFPS) and negative-sequence (FFNS) amplitudes estimation technique is proposed for unbalanced and distorted grid. In this technique, the sequence amplitudes are extracted by extracting the phase-angle of the FFPS and FFNS components. The extracted phase-angles have DC and double frequency AC components. The AC component is filtered out by using a Moving Average Filter (MAF) of appropriate window length. From the extracted phase-angle, the unknown frequency can be estimated by using a suitable controller. A frequency-fixed equidistant samples-based pre-loop filter is also applied to eliminate the effect of measurement offset. The proposed technique has a very simple structure and is easy to tune. Small-signal modeling-based stability analysis and gain tuning procedure are also provided. The proposed technique strikes a good balance between fast convergence and disturbance rejection capability. Comparative numerical simulation and experimental results with similar other techniques demonstrate the suitability and performance enhancement by the proposed technique.

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes.

This article deals with the techno-economic optimal sizing of a tidal stream turbine (TST)–battery system. In this study, the TST system consists of a turbine rotor and a permanent magnet synchronous generator (PMSG) associated with a three-phase converter coupled to a DC bus. A battery is used within the system as an energy storage system to absorb excess produced power or cover power deficits. To determine the optimal sizing of the system, an iterative approach was used owing to its ease of implementation, high accuracy, and fast convergence speed, even under environmental constraints such as swell and wave effects. This technique is based on robust energy management, and the recursive algorithm includes the deficiency of power supply probability (DPSP) and the relative excess power generation (REPG) as technical criteria for the system reliability study, and the energy cost (EC) and the total net present cost (TNPC) as economic criteria for the system cost study. As data inputs, the proposed approach used the existing data from the current speed profile, the load, and economic parameters. The desired output is the system component optimal sizing (TST power, and battery capacity). In this paper, the system sizing was studied during a one-year time period to ensure a more reliable and economical system. The results are compared to well-known methods such as genetic algorithms, particle swarm optimization, and software-based (HOMER) approaches. The optimization results confirm the efficiency of the proposed approach in sizing the system, which was simulated using real-world tidal velocity data from a specific deployment site.

This paper deals with the stabilization problem of a nonlinear system described by a Takagi–Sugeno fuzzy (TSF) model with unmeasurable premise variables via a robust controller. Applying the sector nonlinearity techniques, the nonlinear system is represented by a decoupled fuzzy model. Then, we design a robust observer-based controller for the obtained fuzzy system by utilizing the differential mean value approach. The observer and controller gains are obtained by the separation principle, in which the problem is solved in the sum of linear matrix inequalities (LMIs). The paper presents two main contributions: A state feedback controller is designed using differential mean value (DMVT) which ensures robust stabilization of the nonlinear system. Additionally, the Luenberger observer is extended to the Takagi–Sugeno fuzzy models. The second contribution is to reduce conservatism in the obtained conditions, a non-quadratic Lyapunov function (known as the line integral Lyapunov fuzzy candidate (LILF)) is employed. Two examples are provided to further illustrate the efficiency and robustness of the proposed approach; specifically, the Takagi–Sugeno fuzzy descriptor of an induction motor is derived and a robust observer-based controller applied to the original nonlinear system.

Around the world, the development of electric vehicles is underway, including in maritime transportation. However, the development of clean energy vessels still has a long way to go. Fuel cells (FCs) are a relevant choice among the many clean energy sources to power clean energy vessels. However, due to the complex and drastic change in the shipload power, FCs need to be equipped with dynamic fast-response energy storage equipment to make up for it. For multiple energy storage devices connected in parallel, the state of charge (SOC) is not balanced, which affects their service life and the stability of the vessel microgrid, as well as slowing the speed and lowering the accuracy of SOC equalization. This paper proposes a distributed variable sag slope control strategy for vessels to improve SOC equalization, with a FC as the energy source and a battery and supercapacitor as the energy storage system (ESS). For the output power distribution problem of energy storage equipment caused by shipload power variation, a power distribution strategy with a variable filter time constant is used to improve the reasonableness of the output power distribution of energy-based lithium batteries and power-based supercapacitors. Meanwhile, this paper considers the power generation equipment’s service life and energy cost as the optimization objectives, considering the discharge depth of the energy storage equipment. Finally, a method based on the combination of the lithium battery SOC rule (the rule formulated according to the state of charge and load power change in energy storage equipment) and particle swarm optimization algorithm is proposed to solve this problem. The simulation results show that the proposed strategy improves the equalization speed and accuracy of the SOC of energy storage devices, fully realizes the characteristics of different energy storage devices, and reduces the life loss of energy storage devices.

In this paper, a simplified and robust control strategy of an interleaved high gain DC/DC boost converter (IHGBC) is proposed in order to enhance DC bus voltage regulation in proton exchange membrane fuel cell (PEMFC) applications. The fluctuation of the energy source voltage and external load, and the change in system parameters lead to the instability of output voltage. Based on the creation of an average state space model of the DC/DC boost converter, the proposed controller is designed based on a linear active disturbance rejection control (LADRC), which has an external voltage loop and an internal current loop to meet the output voltage requirements under parameters uncertainties and disturbances. The effectiveness of the proposed approach strategy and its superiority were examined under different operating conditions and scenarios. Simulation and experiment results showed the efficiency and robustness of the suggested approach and the great effectiveness in the reference tracking and disturbance rejection.

Machine learning prognosis for condition monitoring of safety-critical systems, such as aircraft engines, continually faces challenges of data unavailability, complexity, and drift. Consequently, this paper overcomes these challenges by introducing adaptive deep transfer learning methodologies, strengthened with robust feature engineering. Initially, data engineering encompassing: (i) principal component analysis (PCA) dimensionality reduction; (ii) feature selection using correlation analysis; (iii) denoising with empirical Bayesian Cauchy prior wavelets; and (iv) feature scaling is used to obtain the required learning representations. Next, an adaptive deep learning model, namely ProgNet, is trained on a source domain with sufficient degradation trajectories generated from PrognosEase, a run-to-fail data generator for health deterioration analysis. Then, ProgNet is transferred to the target domain of obtained degradation features for fine-tuning. The primary goal is to achieve a higher-level generalization while reducing algorithmic complexity, making experiments reproducible on available commercial computers with quad-core microprocessors. ProgNet is tested on the popular New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset describing real flight scenarios. To the extent we can report, this is the first time that all N-CMAPSS subsets have been fully screened in such an experiment. ProgNet evaluations with numerous metrics, including the well-known CMAPSS scoring function, demonstrate promising performance levels, reaching 234.61 for the entire test set. This is approximately four times better than the results obtained with the compared conventional deep learning models.

Applied Sciences has reached a remarkable milestone by publishing its 10th volume in 2020 [...]

Maximum power point tracking (MPPT) techniques for wind turbines have a significant effect on renewable energy production. Therefore, the association of the indirect torque control of the switched reluctance generator (SRG) with the wind turbine considering the MPPT technique has been developed in this work. The proposed strategy has a great impact on the production of renewable energy using an SRG machine. The main steps to achieve the object of this work are: First, the wind turbine was modeled and simulated according to the MPPT. In the second step, the indirect torque control strategy, based on the hysteresis current control for SRG 12/8, was realized. This was undertaken using a proportional integral regulator and the hysteresis controller for the torque in order to obtain the appropriate switching based on an asymmetric bridge converter. Moreover, the SRG has high nonlinear characteristics. Thus, the modeling results of this kind of machine are obtained by the use of the finite element method, with its dynamic study performed by the unique estimation of the electromagnetic torque in its generator mode functioning. Finally, the indirect torque control technique of the SRG has also been associated with the MPPT technique to maximize the efficiency power coefficient. The obtained results approve and validate the efficiency of a proposed MPPT of the wind turbine associated with the SRG. This illustrates, simultaneously, the remarkable effects of the turn-switching angles on the operating performances and the high quality of the produced energy. The importance of the effect of varying the turn-switching angles is also presented and discussed.

The wave compensation system can be very useful in several naval applications. It can greatly reduce the relative irregular motion between the two ships when replenishment operations are performed, or between the ship and the offshore platform, which is caused by the waves. It is widely used in offshore operations, offshore cargo transfer, oil and gas exploitation, deep-sea mining, the hoisting and recovery of submersibles, etc. However, when a crane is used in a ship or moving platform, due to the influence of the hull, the crane load movement is similar to a space ball pendulum, which causes the heave displacement to show significant nonlinear motion characteristics. Moreover, the time delay of the detection mechanism and control error could result in untimely compensation, which deteriorates the performance. Consequently, this paper proposes one advanced prediction compensation method, namely Particle Swarm Optimized Model Predictive Trajectory Path controller (PSO−MPTP), which can improve the heave compensation performance. This method, which is based on Model Predictive Control (MPC), is firstly applied to the position servo system and takes into account the heave prediction and control effects simultaneously. The heave displacement of the crane load could be predicted in multiple steps in advance and used as the input of the position loop of the compensation machine. The achieved simulations show that the proposed controller has better prediction ability, higher control accuracy, and stronger robustness.