Search Results (41)

Iterative learning control (ILC) typically requires strict repeatability in initial states, trajectory length, external disturbances, and system dynamics. However, these assumptions are often difficult to fully satisfy in practical applications. While most existing studies have achieved limited progress in relaxing either one or two of these constraints simultaneously, this work aims to eliminate the restrictions imposed by all four strict repeatability conditions in ILC. For general finite-duration multi-input multi-output (MIMO) linear discrete-time systems subject to multiple non-repetitive uncertainties—including variations in initial states, external disturbances, trajectory lengths, and system dynamics—an innovative open-closed loop robust iterative learning control law is proposed. The feedforward component is used to make sure the tracking error converges as expected mathematically, while the feedback control part compensates for missing tracking data from previous iterations by utilizing real-time tracking information from the current iteration. The convergence analysis employs an input-to-state stability (ISS) theory for discrete parameterized systems. Detailed explanations are provided on adjusting key parameters to satisfy the derived convergence conditions, thereby ensuring that the anticipated tracking error will eventually settle into a compact neighborhood that meets the required standards for robustness and convergence speed. To thoroughly assess the viability of the proposed ILC framework, computer simulations effectively illustrate the strategy’s effectiveness. Further simulation on a real system, a piezoelectric motor system, verifies that the ILC tracking error converges to a small neighborhood in the sense of mathematical expectation. Extending the ILC to complex real-world applications provides new insights and approaches. Full article

In a modern flexible interconnected distribution network, the dynamic coupling effect between the traditional AC network model and the power electronic converter significantly enhances the nonlinearity and non-convexity of power flow calculations. In particular, when a one-end converter station quits operating due to a fault, it is necessary to ensure that the remaining converter stations can continue to maintain the normal operation of the interconnected system, which leads to the convergence problem of the traditional physical-driven iterative method. Aiming to address this problem, this study discusses the data-driven linearization method of the current distribution network power flow in depth and proposes a linearized power flow calculation (LPFC) of a flexible interconnected distribution network based on a data–physical fusion drive. Based on the traditional linearization method based on physical characteristics and first-order Taylor expansion, the model uses the partial least squares method to compensate for the linearization error and can normally cope with the failure of the flexible interconnected system. The proposed model greatly improves the convergence and computational efficiency of the power flow model under the premise of ensuring the linearization accuracy and can adapt to different load levels to achieve accurate error compensation. In addition, based on an actual engineering example, this paper introduces the converter station model, constructs a flexible interconnected system, and verifies the applicability of the proposed model. Full article

This work provides a new solution for high-power quality traction power systems. The rapid development of electrified railways not only promotes economic development, but also seriously restricts the improvement of electric locomotive operation performance due to power quality problems, such as second harmonic distortion and negative sequence in the power supply system. In view of the shortcomings of the traditional in-phase power supply system in DC bus voltage stability control, a new in-phase power supply topology based on a back-to-back H-bridge power supply unit is proposed in this study. By establishing the iterative analysis model of the rectifier side double closed-loop control system, the internal correlation mechanism between the DC bus voltage second harmonic fluctuation and the grid side current harmonic is deeply revealed. On this basis, a rectifier-side disturbance compensation control strategy with a second harmonic suppression function is designed. Through real-time detection and compensation of second harmonic components, the active stability control of DC bus voltage is realized. The simulation model of the new cophase power supply system based on the experimental platform shows that the strategy can reduce the ripple coefficient of the DC bus voltage and the total harmonic distortion of the grid side current, which effectively verifies the superiority of the second harmonic suppression strategy in improving the power quality of the cophase power supply system. This work provides a new solution for a high-power quality traction power system. Full article

Holographic detection technology has found extensive applications in biomedical imaging, surface profilometry, vibration monitoring, and defect inspection due to its unique phase detection capability. However, the accuracy of quantitative holographic phase imaging is significantly affected by the interference from direct current and twin image terms. Traditional methods, such as multi-exposure phase shifting and off-axis holography, have been employed to mitigate these interferences. While off-axis holography separates spectral components by introducing a tilted reference beam, it inevitably induces phase disturbances that compromise measurement accuracy. This study provides a computational explanation for the incomplete phase compensation issue in existing algorithms and establishes precision criteria for phase compensation based on theoretical formulations. We propose two novel phase compensation methods—the non-iterative compensation approach and the multi-iteration compensation technique. The principles and applicable conditions of these methods are thoroughly elucidated, and their superiority is demonstrated through comparative experiments. The results indicate that the proposed methods effectively compensate for phase disturbances induced by the tilted reference beam, offering enhanced precision and reliability in quantitative holographic phase measurements. Full article

In the rapidly evolving field of computer vision and machine learning, 3D skeleton estimation is critical for applications such as motion analysis and human–computer interaction. While stereo cameras are commonly used to acquire 3D skeletal data, monocular RGB systems attract attention due to benefits including cost-effectiveness and simple deployment. However, persistent challenges remain in accurately inferring depth from 2D images and reconstructing 3D structures using monocular approaches. The current 2D to 3D skeleton estimation methods overly rely on deep training of datasets, while neglecting the importance of human intrinsic structure and the principles of camera imaging. To address this, this paper introduces an innovative 2D to 3D gait skeleton estimation method that leverages the Brown camera distortion model and constrained optimization. Utilizing the Azure Kinect depth camera for capturing gait video, the Azure Kinect Body Tracking SDK was employed to effectively extract 2D and 3D joint positions. The camera’s distortion properties were analyzed, using the Brown camera distortion model which is suitable for this scenario, and iterative methods to compensate the distortion of 2D skeleton joints. By integrating the geometric constraints of the human skeleton, an optimization algorithm was analyzed to achieve precise 3D joint estimations. Finally, the framework was validated through comparisons between the estimated 3D joint coordinates and corresponding measurements captured by depth sensors. Experimental evaluations confirmed that this training-free approach achieved superior precision and stability compared to conventional methods. Full article

The aim of the study is to quantify the impacts of a possible transition to close-to-nature forestry in Slovakia and to compare the expected development of the total volume production, growing stock, merchantable wood increment and harvesting possibilities of forests in Slovakia with current conventional management using the FCarbon forest-growth model and available data from the Information System of Forest Management. The subject of the study was all forest stands available for wood supply (FAWS). The simulations were run in annual iterations using tree input data aggregated over 10-year-wide age classes. The calculation of wood increments was based on available growth models. In the business-as-usual (BAU) scenario, stock losses were based on the actual intensity of wood harvesting in the reference period 2013–2022. In the scenario of the transition to close-to-nature forest management, the losses were specifically modified from the usual harvesting regime at the beginning, to the target harvesting mode in selective forest at the end of the simulated period. With the modelling method used, a gradual increase in forest stocks occurred in both evaluated scenarios in the monitored period, namely by 10% in the case of BAU and by 23% in the case of close-to-nature forest management until 2050. In absolute mining volume, CTNF is by 5–10% lower than BAU management, with the difference gradually decreasing. The results show that the introduction of close-to-nature forest management will temporarily reduce the supply of wood to the market, but this reduction will not be significant and will be compensated by a higher total volume production, and thus also by increased carbon storage in forests. Full article

Iterative learning based digital peak current mode control (PCMC) is proposed in this paper. The proposed control method provides excellent current reference tracking against variations in input voltage, load, and circuit parameters. Compared to other current control methods, the proposed digital PCMC has a high dynamic response, a simple structure and a low computational burden. It is suitable for power factor correction (PFC) converters operating at high frequency. Thanks to the iterative learning control (ILC), the peak current value in PCMC is successfully compensated against disturbances. The proposed new current control method is applied to an interleaved totem pole PFC (ITPPFC) circuit. The ITPPFC circuit prototype is implemented with 250 W output power and 100 kHz switching frequency. The circuit prototype is tested under various load conditions and parametric disturbances. Theoretical and experimental results are found to be consistent. Full article

Magnetic suspension balance systems (MSBSs) need to allow vehicle models to levitate stably in different attitudes, so it is difficult to ensure the stable performance of the system under different levitation gaps using a controller designed with single balance point linearization. In this paper, a levitation controller based on linear model predictive control and a back-propagation neural network (LMPC-BP) is proposed and simulated for single-point magnetic levitation. The deviation of the BP network is observed and compensated by an expansion state observer (ESO). The iterative BP neural network model is further updated using current data and feedback data from the ESO, and then the performance of the LMPC-BP controller is evaluated before and after the update. The simulation results show that the LMPC-BP controller can achieve stable levitation at different gaps of the single-point magnetic levitation system. With further updating and iteration of the BP network, the controller anti-jamming performance is improved. Full article

Accurate and timely information on farmland size is crucial for agricultural development, resource management, and other related fields. However, there is currently no mature method for estimating farmland size in smallholder farming areas. This is due to the small size of farmland plots in these areas, which have unclear boundaries in medium and high-resolution satellite imagery, and irregular shapes that make it difficult to extract complete boundaries using morphological rules. Automatic farmland mapping algorithms using remote sensing data also perform poorly in small-scale farming areas. To address this issue, this study proposes a farmland size evaluation index based on edge frequency (ECR). The algorithm utilizes the high temporal resolution of Sentinel-2 satellite imagery to compensate for its spatial resolution limitations. First, all Sentinel-2 images from one year are used to calculate edge frequencies, which can divide farmland areas into low-value farmland interior regions, medium-value non-permanent edges, and high-value permanent edges (PE). Next, the Otsu’s thresholding algorithm is iteratively applied twice to the edge frequencies to first extract edges and then permanent edges. The ratio of PE to cropland (ECR) is then calculated. Using the North China Plain and Northeast China Plain as study areas, and comparing with existing farmland size datasets, the appropriate estimation radius for ECR was determined to be 1600 m. The study found that the peak ECR value for the Northeast China Plain was 0.085, and the peak value for the North China Plain was 0.105. The overall distribution was consistent with the reference dataset. Full article

The improvement of efficiency in new and existing buildings is one of the key aspects in achieving the climate change targets promoted by international regulatory and technical bodies, and among the measures that deserve renewed attention is the balancing of hydronic systems. However, the balancing procedures currently applied have not been updated for decades and are still largely unimplemented, as they are mainly based on cumbersome and iterative procedures. This paper deals with the proposal and advanced adaptation of a non-iterative balancing method previously developed for air systems, known as the progressive flow method (PFM). The application to water systems of the PFM’s concepts includes some aspects of an existing empirical method called the compensated method (CM) and overcomes its main limitations; moreover, the original PFM has been radically rethought in its implementation aspects, taking advantage of the tightness of water distribution systems, minimising instrumentation and the number of measurement operations, to definitively overcome the iterative nature of the currently applied methods. Experimental validation was carried out. Compared with a standard method, the enhanced PFM reduced the number of measurements by 48% and the number of balancing operations by 41%, achieving final flow rates within tolerances and the same configuration of balancing devices. Full article

Underwater localization is one of the key techniques for positioning, navigation, timing (PNT) services that could be widely applied in disaster warning, underwater rescues and resource exploration. One of the reasons why it is difficult to achieve accurate positioning for underwater targets is due to the influence of uneven distribution of underwater sound velocity. The current sound-line correction positioning method mainly aims at scenarios with known target depth. However, for nodes that are non-cooperative nodes or lack depth information, sound-line tracking strategies cannot work well due to non-unique positional solutions. To solve this problem, we propose an iterative ray tracing 3D underwater localization (IRTUL) method for stratification compensation. To demonstrate the feasibility of fast stratification compensation, we first derive the signal path as a function of initial |grazing angle, and then prove that the signal propagation time and horizontal propagation distance are monotonic functions of the initial grazing angle, which guarantees the fast achievement of ray tracing. Simulation results indicate that IRTUL has the most significant correction effect in the depth direction, and the average accuracy has been improved by about 3 m compared to a localization model with constant sound velocity. Full article

Aiming to reduce motor speed estimation and torque vibration present in the permanent magnet synchronous motors (PMSMs) of rim-driven thrusters (RDTs), a position-sensorless control algorithm using an adaptive second-order sliding mode observer (SMO) based on the super-twisting algorithm (STA) is proposed. In which the sliding mode coefficients can be adaptively tuned. Similarly, an iterative learning control (ILC) algorithm is presented to enhance the robustness of the velocity adjustment loop. By continuously learning and adjusting the difference between the actual speed and given speed of RDT motor through ILC algorithm, online compensation for the q-axis given current of RDT motor is achieved, thereby suppressing periodic speed fluctuations during motor running. Fuzzy neural network (FNN) training can be used to optimize the STA-SMO and ILC parameters of RDT control system, while improving speed tracking accuracy. Finally, simulation and experimental verifications have been conducted on the vector control system based on the conventional PI-STA and modified ILC-STA. The results show that the modified algorithm can effectively suppress the estimated speed and torque ripple of RDT motor, which greatly improves the speed tracking accuracy. Full article

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the foundation for high-performance driving in predictive control systems. The traditional PMSM multi-parameter identification method suffers from insufficient rank of the identification equation and is prone to getting stuck in local optimal solutions. This article combines the bacterial foraging optimization algorithm (BFOA) to establish a built-in PMSM predictive control parameter compensation model. Firstly, we analyzed the reasons why the distortion of PMSM motor parameters affects the actual speed and calculated the deviation of d-axis and q-axis currents caused by the distortion. Secondly, parameter compensation was applied to the prediction model, and BFOA was combined to optimize the compensation parameters. This algorithm does not use the traditional voltage equation as the fitness function but instead uses a brand-new set of four equations for parameter iteration optimization. The optimized compensation parameters can reduce current deviation and improve the robustness of the PMSM predictive control system. The proposed model can cover four kinds of motor distortion parameters, including stator resistance, D-axis inductance, Q-axis inductance, and permanent magnet flux linkage. Finally, the traditional PMSM predictive control model is compared with the predictive control model combined with BFOA. The simulation results show that the dynamic and static performance of the compensated system is improved when single or multiple parameters are distorted. Full article

Periodic torque ripple often occurs in permanent magnet synchronous motors due to cogging torque and flux harmonic distortion, leading to motor speed fluctuations and further causing mechanical vibration and noise, which seriously affects the performance of the motor vector control system. In response to the above problems, a PMSM torque ripple suppression method based on SMA-optimized ILC is proposed, which does not rely on prior knowledge of the system and motor parameters. That is, an SMA is used to determine the optimal values of the key parameters of the ILC in the target motor control system, and then the real-time torque deviation value calculated by iterative learning is compensated to the system control current set end. By reducing the influence of higher harmonics in the control current, the torque ripple is suppressed. Research results show that this method has high efficiency and accuracy in parameter optimization, further improving the ILC performance, effectively reducing the impact of higher harmonics, and suppressing the torque ripple amplitude. Full article

Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed by sea clutter. Sea clutter increases the difficulty of azimuth estimation, resulting in a challenging problem for shipborne HFSWR. To solve this problem, a novel azimuth correction method based on adaptive boosting error feedback dynamic weighted particle swarm optimization extreme learning machine (APO-ELM) is proposed to improve the azimuth estimation accuracy of shipborne HFSWR. First, the sea clutter is modeled and simulated. Then, we study its characteristics and analyze the influence of its characteristics on the first-order clutter spectrum and target detection accuracy, respectively. In addition, the proposed improved particle swarm optimization (PSO) and adaptive neuron clipping algorithm are used to optimize the input parameters of the ELM network. Then, the network performs error feedback based on the optimized parameter performance and updates the feature matrix, which can give a minimum clutter-error estimation. After that, it iteratively trains multiple weak learners using the adaptive boosting (AdaBoost) algorithm to form a strong learner and make strong predictions. Finally, after error compensation, the best azimuth estimation results are obtained. The sample sets used for the APO-ELM network are obtained from field shipborne HFSWR data. The network training and testing features include the wind direction, sea current, wind speed, platform speed, and signal-to-clutter ratio (SCR). The experimental results show that this method has a lower root-mean-square error than the back-propagation neural network and support vector regression (SVR) azimuth correction methods, which verifies the effectiveness of the proposed method. Full article