Machines, Volume 12, Issue 5 (May 2024) – 34 articles

Various conditions, including traffic accidents, sports injuries, and neurological disorders, can impair human wrist movements, underscoring the importance of effective rehabilitation methods. Robotic devices play a crucial role in this regard, particularly in wrist rehabilitation, given the complexity of the human wrist joint, which encompasses three degrees of freedom: flexion/extension, pronation/supination, and radial/ulnar deviation. This paper provides a comprehensive review of wrist rehabilitation devices, employing a methodological approach based on primary articles sourced from PubMed, ScienceDirect, Scopus, and IEEE, using the keywords “wrist rehabilitation robot” from 2007 onwards. The findings highlight a diverse array of wrist rehabilitation devices, systematically organized in a tabular format for enhanced comprehension. Serving as a valuable resource for researchers, this paper enables comparative analyses of robotic wrist rehabilitation devices across various attributes, offering insights into future advancements. Particularly noteworthy is the integration of serious games with simplified wrist rehabilitation devices, signaling a promising avenue for enhancing rehabilitation outcomes. These insights lay the groundwork for the development of new robotic wrist rehabilitation devices or to make improvements to existing prototypes incorporating a forward-looking approach to improve rehabilitation outcomes. Full article

In this study, the fatigue strength of polished drill pipe specimens was investigated and compared with previous test results of corroded and not-corroded pipes. The resonant fatigue test rig, which was designed and implemented by the University of Pisa, is initially presented by providing a detailed description of the set-up of the machine, the calibration of the strain gauges, the control system, and the correct identification of the vibrational node locations. A polishing rig was also designed and put into operation to remove the corrosion pits from the outer surface of almost the entire length of the drill pipe specimens. After the fatigue tests with the resonant rig, and the observation of the fatigue fracture of the specimens, a few samples were extracted from different zones (corroded and not corroded) of the failed drill pipe specimens. This allowed for investigations to be carried out using a scanning electronic microscope. The obtained results were analyzed using the Murakami model, and a discussion is presented about the effect of the corrosion pits on the fatigue strength. Full article

High-precision tapered threads are widely used in hard-loaded mechanical joints, especially in the aggressive environment of the drilling of oil and gas wells. Therefore, they must be made of workable materials often difficult to machine. This requires the use of high-performance cutting tools, which means the application of non-zero geometric parameters: rake and edge inclination angles. This study is based on analytical geometry methodology and describes the theoretical function of the thread profile as convoluted surfaces dependent on the tool’s geometric angles. The experiments were conducted using a visual algorithm grounded on the obtained function and prove the practical use of the scientific result. They predict the required accuracy of thread made using a lathe tool with a rake angle of up to 12°. Full article

When placed under cryogenic temperatures (below −180 °C), metallic materials undergo structural changes that can improve their service life. This process, known as cryogenic treatment (CrT), has received extensive research attention over the past five decades. CrT can be applied as either an autonomous process (for steels and non-ferrous alloys, tool materials, and finished products) or as an assisting process for conventional metalworking. Cryogenic impacts and conventional machining or static surface cold working (SCW) can also be performed simultaneously in hybrid processes. The static SCW, known as burnishing, is a widely used environmentally friendly finishing process that achieves high-quality surfaces of metal components. The present review is dedicated to the portion of the hybrid processes in which burnishing under cryogenic conditions is carried out from the viewpoint of surface engineering, namely, finishing–surface integrity (SI)–operational behavior. Analyzes and summaries of the effects of cryogenic-assisted (CrA) burnishing on SI and the operational behavior of the investigated materials are made, and perspectives for future research are proposed. Full article

The article is aimed at identifying the influence of laser texturing and subsequent coating with a hard, wear-resistant coating AlCrSiN (nACRo^®) on selected tribological properties of the analyzed tool steels for cold work, produced by conventional and powder metallurgy. The substrate from each steel was heat treated to achieve optimal properties regarding the chemical composition and the method of production of the material. Böhler K100 and K390 Microclean^® steels were used. These are highly alloyed tool steels used for various types of tools intended for cold work. The obtained results show that the coefficient of friction is increased by coating, but the wear rate is lower compared to the samples which were only textured. Full article

In digital product and ion scheduling centers, order–factory allocation, factory–mold allocation, and mold routing can be performed centrally and efficiently to maximize the utilization of manufacturing resources (molds). Therefore, in this paper, a manufacturing resource (molds)-sharing mechanism based on the Internet of Things (IoT) and a cyber-physical production system (CPPS) is designed to realize the coordinated allocation of molds and production scheduling. A mixed-integer mathematical model is developed to optimize the cost structure and obtain a reasonable profit solution. A heuristic algorithm based on evolutionary reversal is used to solve the problem. The numerical results show that based on the digital coordinated production scheduling method, distributed two-stage assembly manufacturing with shared molds can effectively reduce the order delay time and increase potential benefits for distributed production enterprises. Full article

Abnormal detection plays a pivotal role in the routine maintenance of industrial equipment. Malfunctions or breakdowns in the drafting components of spinning equipment can lead to yarn defects, thereby compromising the overall quality of the production line. Fault diagnosis of spinning equipment entails the examination of component defects through Wavelet Spectrogram Analysis (WSA). Conventional detection techniques heavily rely on manual experience and lack generality. To address this limitation, this current study leverages machine learning technology to formulate a semi-supervised anomaly detection approach employing a convolutional autoencoder. This method trains deep neural networks with normal data and employs the reconstruction mode of a convolutional autoencoder in conjunction with Kernel Density Estimation (KDE) to determine the optimal threshold for anomaly detection. This facilitates the differentiation between normal and abnormal operational modes without the necessity for extensive labeled fault data. Experimental results from two sets of industrial data validate the robustness of the proposed methodology. In comparison to conventional Autoencoder and prevalent machine learning techniques, the proposed approach demonstrates superior performance across evaluation metrics such as Accuracy, Recall, Area Under the Curve (AUC), and F1-score, thereby affirming the feasibility of the suggested model. Full article

The primary objective of this research endeavor was to devise an improved workplace design tailored to the demands of a digital factory environment. With the overarching aim of enhancing efficiency and productivity, a comprehensive proposal was formulated to improve layout configurations within the designated enterprise. The key focus lies in minimizing material transit across individual workstations, thereby mitigating potential bottlenecks and streamlining operations. Employing a structured workplace research framework, this study delved into material flow analysis techniques, augmented by the utilization of visTABLE software. While visTABLE served solely to visualize the work environment effectively, it played a crucial role in validating proposed solutions. Notably, the investigation yielded a discernible reduction in beam production time, marking a significant improvement of 10 min. These findings underscored the efficacy of the proposed solutions in addressing specific operational challenges faced by the company. Furthermore, this study facilitated a deeper understanding and visualization of the processes intrinsic to the digital factory environment. Elucidating workflow procedures at the workplace enabled stakeholders to identify areas for further improvement and refinement. In doing so, the research contributed to the overall efficiency and effectiveness of operations within the digital factory, paving the way for continued improvement and innovation in the field. Full article

Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector’s technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis and prediction methods. The endeavor to accurately analyze welding deformation in practical applications is compounded by the interplay of numerous variables, a pronounced coupling effect among these factors, and a reliance on expert intuition. Thus, effective deformation control in automated laser welding operations necessitates the gathering of pre-test laser welding data to develop a predictive approach that accurately reflects real-world conditions and is characterized by improved reliability and stability. To address the technological evolution in automated laser welding, a predictive model based on neural network technology is proposed to map the intricate relationship between process variables and the resulting deformation. At the heart of this approach is the formulation of a predictive model utilizing a back-propagation neural network (BP), with an emphasis on four essential welding parameters: speed, peak power, duty cycle, and defocusing amount. The model’s predictive accuracy is then honed through the application of the whale optimization algorithm (WOA) and the differential evolutionary (DE) algorithm. Finally, extensive testing in an automated laser welding experimental setup is conducted to validate the accuracy and reliability of the proposed prediction model. It is demonstrated through these experiments that the deformation prediction model, enhanced by the DEWOA-BP neural network, accurately forecasts the relationship between laser welding parameters and the induced deformation, maintaining a prediction error margin of ±0.1mm. The model is employed to fulfill the requirements for a pre-welding quality evaluation, thereby facilitating a more calculated and informed approach to welding operations. This method of intelligent prediction is not only crucial for the intelligent transformation of laser welding but also holds significant implications for traditional machining technologies such as milling, grinding, and spraying. It offers innovative ideas and methods that are pivotal for the industrial revolution and technological advancement of the traditional machining industry. Full article

Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due to non-uniform materials in the workpiece, making it difficult to accurately monitor tool condition by relying on instantaneous signals. To reduce the impact of transient fluctuations, this paper proposes a novel network based on deep learning to monitor and predict tool wear. Firstly, a CNN model based on residual connection was designed to extract deep features from multi-sensor signals. After that, a temporal model based on an encoder and decoder was built for short-term monitoring and long-term prediction. It captured the instantaneous features and long-term trend features by mining the temporal dependence of the signals. In addition, an encoder and decoder-based temporal model is proposed for smoothing correction to improve the estimation accuracy of the temporal model. To validate the performance of the proposed model, the PHM dataset was used for wear monitoring and prediction and compared with other deep learning models. In addition, CFRP milling experiments were conducted to verify the stability and generalization of the model under different machining conditions. The experimental results show that the model outperformed other deep learning models in terms of MAE, MAPE, and RMSE. Full article

To solve the problem of fault diagnosis for the key components of the CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and an ensemble learning model is proposed in this study. First, various monitoring signals including vibration signals, noise signals, and current signals are collected. Then, the monitoring signals are preprocessed and the time domain, frequency domain, and time–frequency domain feature indices are extracted to construct a multi-dimensional mixed-domain feature set. Finally, the feature set is entered into the constructed DoubleEnsemble–LightGBM model to realize the fault diagnosis of the key components of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the widely used dataset and the feed system test bench dataset, and the average overall accuracy is 91.07% and 98.06%, respectively. Compared with XGBoost and other advanced ensemble learning models, this method demonstrates better accuracy. Therefore, the proposed method provides technical support for the stable operation and intelligence of CNC machines. Full article

Calculating the service life of gears under variable loads requires a description of the load-carrying capacity. The current standard for this is the use of the S/N curve. International standards such as ISO 6336 stipulate the use of this approach for the calculation of the service of gears under variable loads. In this paper, five new approaches are developed and evaluated to describe the load-carrying capacity of gears in the load range of finite life. Four methods are based on machine learning, and one uses mathematical regression. To validate the new approaches, the results of an experimental study investigating the service life of gears under variable loads are presented. These results form the basis for the conducted study, which compares the five new methods with the existing approach. The comparison focuses on the ability of the load-carrying capacity descriptions to provide an accurate calculation of the service life and to reduce scattering as much as possible. The results of the study show significant potential for the new methods, especially the one based on a neural network. Full article

With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship’s power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 $\Omega$, 10 $\Omega$ and 15 $\Omega$ ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability. Full article

Vibration in hot tandem rolling mills has been a problem in the iron and steel industry mainly due to its unpredictability. In this work, vibration data of the second finishing mill (F2) stand of a hot tandem rolling mill are collected and analyzed, and a mathematical model based on the coupling of a non-uniform deformation process, mill structure and hydraulic control system is constructed. The influence of different inlet thickness fluctuation forms, structural parameters and control parameters on the vibration behavior is analyzed. It is concluded that the low-frequency thickness fluctuation with additional skewness can cause the resonance of each subsystem of the rolling mill. The deviation angle of the roll system influences the vibration harmonic output of the rolling mill under a single low-frequency thickness fluctuation excitation. The compensation parameter in the thickness control system affects the natural frequency of the vertical system. Full article

This study aims to comprehensively assess the lubrication effect of leaves on wheel–rail contact dynamics using the Virginia Tech-Federal Railroad Administration (VT-FRA) Roller Rig, which closely simulates field conditions with precision and repeatability. Railway operators grapple with the seasonally recurring challenge of leaf contamination, which can cause partial loss of braking and lead to undesired events such as station overruns. Better understanding the adhesion-reducing impact of leaf contamination significantly improves railway engineering practices to counter their effects on train braking and traction. This experimental study evaluates the reduction in traction and braking forces (collectively called “adhesion”) as a function of leaf volume, using two leaf species that commonly grow along U.S. railroad tracks. The test methods rely on the chosen leaves’ transpiration characteristics while ensuring the result’s reproducibility. Leaves were symmetrically positioned on the wheel surface, centered around the mid-rib area within the wear band, and taped on the edges far from the wear band. The critical test parameters (i.e., wheel load, wheel velocity, and percentage creepage) are kept constant among the tests. At the same time, leaf volume is reduced from a maximum amount that covers the entire wheel surface (100% coverage) to no leaves (0%). The latter is used as the baseline. The percentage creepage is kept constant at an exaggerated amount of 2% to accelerate the test time. The results indicate a nonlinear relationship between leaf volume and the loss of braking. Even a small amount of leaf contamination causes a significant reduction in adhesion by as much as 50% compared with no contamination (i.e., baseline). Increasing leaf volume results in contact saturation, beyond which adhesion is not reduced. The minimum adhesion observed in this study is 20% of the maximum adhesion that occurs when no leaf contamination is present. Full article

In order to study the effect of the pumping depth and pumping frequency of the flapping hydrofoil device on suspended solids in the waters, this paper takes raceway aquaculture as an example, and introduces a flapping hydrofoil device to improve the discharge of suspended solids in the raceway, in response to the problem of the deposition of suspended solids from fish faeces and bait residues in water. The CFD method was used to compare and analyze the discharge of suspended solids at different pumping depths, and the combined effect of the two was studied according to different combinations of pumping frequency and pumping depth. The results proved that the flapping hydrofoil motion can improve the bottom hydrodynamic insufficiency in ecological waters and thus enhance the discharge effect of suspended particles in water. In addition, the pumping depth of the flapping hydrofoil is too deep for the movement to be disturbed by the bottom surface, while the thrust generated by the flapping hydrofoil is weakened if the depth is too shallow. When the pump water depth is 1.1 H, the reversed Kármán vortex street is more stable under the balancing effect of the bottom surface and gravity, and the rate curve of the flapping hydrofoil acting on the discharge of suspended particles is better. From our comprehensive consideration of the joint effect of the pumping depth and pumping frequency, we recommend the use of a 1.1 H of pumping depth and 2.0 Hz pumping frequency in combination to achieve the best effect of discharging suspended particles. This study provides valuable insights into the actual engineering applications of flapping hydrofoil devices for improving water quality and ecological sustainability in raceway aquaculture. Full article

Four-bar linkages are critical fundamental elements of many mechanical systems, and their design synthesis is often mathematically complicated with iterative numerical solutions. Analytical methods based on Fourier coefficients can circumvent these difficulties but have issues with time parameters assignment for path generation without prescribed time in previous studies. In this paper, an improved Fourier-based point-to-point combination method is presented, which generates more points by Fourier approximation and assigns the time parameters to the given points while allowing discarding solutions with order defects. This method relies on the Fourier coefficients, improving the accuracy of synthesis solutions, and simplifying the computational procedure. Time parameters are assigned directly to the given points, which avoids the complex calculations to find intersection points in the given path, eliminates combinations that would lead to solutions with order defects, and simplifies the assessment process of synthesis results. The parameters obtained by the point-to-point combination method can be used as the parameters of the input dyad, skipping the first set of design equations for faster calculation. Several examples are presented to demonstrate the advantages of the proposed synthesis method, which is easy-understanding, computationally efficient, and yields more accurate solutions than available synthesis methods. Full article

A walking trajectory tracking control approach for a walking electrohydraulic control system is developed to reduce the walking trajectory tracking deviation and enhance robustness. The model uncertainties are estimated by a designed state observer. A saturation function is used to attenuate sliding mode chattering in the designed sliding mode controller. Additionally, a walking trajectory tracking control strategy is proposed to improve the walking trajectory tracking performance in terms of response time, tracking precision, and robustness, including walking longitudinal and lateral trajectory tracking controllers. Finally, simulation and experimental results are employed to verify the trajectory tracking performance and observability of the model uncertainties. The results testify that the proposed approach is better than other comparative methods, and the longitudinal and lateral trajectory tracking average absolute errors are controlled in 10.23 mm and 22.34 mm, respectively, thereby improving the walking trajectory tracking performance of the walking electrohydraulic control system for the coal mine drilling robot for rockburst prevention. Full article

This article proposes a new deadbeat predictive current control (DPCC) method based on a sliding-mode observer (SMO), which is applied in the field of permanent magnet motor control. A novel DPCC control method based on SMO is proposed according to the inherent issues of DPCC, which can effectively suppress internal parameter mismatch disturbances and external disturbances in the current loop. The mathematical model and derivation process of the proposed method are introduced. A simulation model is built and the effectiveness of the proposed method is verified. An experimental platform is built and the superiority of the proposed method is verified based on comparative experiments. Experimental results show that the proposed algorithm has strong robustness to the motor parameter mismatch. Compared with extended state observer (ESO) and adaptive observer (AO), the proposed algorithm has faster response speed and higher steady-state accuracy. Full article

The stabilisation capabilities of unmanned aerial vehicles (UAVs) with bicopter underactuated swashplateless rotors are highly sensitive to motor-induced vibration. Due to the requirement of the active control of underactuated swashplateless rotors, conventional designs are limited in reducing vibration through control optimisation. A solution with customized passive spring-damping structures on a unique underactuated swashplateless rotor of a tiltrotor bicopter platform is presented. The implementation of this structure effectively reduces the self-coherent vibration in flights. As a result, a higher level of control authority has been achieved without setting excessive low-pass filtering for vibration. Experimentally obtained inertial measurement unit (IMU) data, rotor speed, rotor tilt angle, and the cyclic stator response are presented for comparison with Simulink model predictions. Full article

The traditional PID controller in quadrotor UAVs has poor performance, a large overshoot, and a long adjustment time, which limit its stability and accuracy in practical applications. In order to solve this problem, an improved whale optimization fuzzy PID control strategy based on CRICLE chaos map initialization is proposed, and a detailed simulation analysis was carried out using MATLAB software (MATLAB R2022B). Firstly, to more realistically reflect quadrotor UAVs’ flight behavior, a dynamic simulation model was established, and the dynamics and kinematic characteristics of the aircraft were considered. Then, CRICLE chaotic mapping initialization was introduced to improve the global search ability of the whale optimization algorithm and to effectively initialize the parameters of the fuzzy PID controller. This improved initialization method helped to speed up the convergence process and improve the stability of the control system. In the simulation experiments, we compared the performance indicators of the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID controller to the traditional PID and fuzzy PID controllers, including overshoot, adjustment time, etc. The results show that the proposed control strategy has better performance than the traditional PID and fuzzy PID controllers, significantly reduces overshoot, and achieves a significant improvement in adjustment time. Therefore, the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID control strategy proposed in this study provides an effective solution for improving the performance of the quadrotor control system and has practical application potential. Full article

Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome . Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies. Full article

The growth of electrical machine applications in high-torque environments such as marine propulsion and wind energy is encouraging the development of higher-power-density machines at ever higher efficiencies and under competitive pressure to meet higher demands. In this study, numerical simulations are performed to investigate the characteristics of air cooling applied to a 3 MW high-torque internal permanent magnet electric machine with integrated power electronics. The whole system of the main machine and two converters at either end are modelled with all details. Effects of different parameters on the total pressure drop and air flow rate to the machine and converters are examined. Results show that by changing the converter outlet hole size, the air flow rate to the machine and converter can be adjusted. Air guides and pin vents reveal excellent performance in the distribution of air to laminations and windings with a penalty of some increase in pressure drop, which is more pronounced when using smaller outlet holes. Furthermore, the air return manifold increases the pressure drop and causes a reduction in air flow rate to the converter. Insulation between compression plate and laminations is an unavoidable component used in electric machines and acts as a thermal insulator. However, it can also significantly augment pressure drop, especially in combination with smaller outlet holes. Thermal studies of the integrated power electronics illustrate that components’ temperatures are less than the temperature limit, confirming enough air through the converter. Analysis of power electronics in the case of fan failure provides the operational time window for the operators to respond. Full article

Common-mode voltage (CMV) brings shaft voltage and shaft current, and corrodes the bearings of the permanent-magnet synchronous machine (PMSM), which affects the reliability of the whole PMSM drive system. Since the CMV applied by the zero voltage vectors (ZVVs) is three times that applied by the active voltage vectors (AVVs), a modulation scheme achieving minimum CMV without ZVV is proposed and introduced into the model predictive control structure for the PMSM drive system. Firstly, the whole modulation range is divided into three regions, including the low voltage modulation region (LVMR), high voltage modulation region (HVMR), and over-voltage modulation region (OVMR). Meanwhile, the regional boundary expression is derived. Then, the active zero-state pulse width modulation (AZSPWM) is adopted in LVMR. To improve the steady-state performance, near-state pulse width modulation (NSPWM) without opposite ZVVs is applied to the HVMR. Furthermore, when the reference voltage vector (VV) is located in OVMR, an optimal scheme is proposed to improve the dynamic response. Under the premise of no ZVV existing in the whole modulation region, simulation and experimental results show that the proposed hybrid modulation method can improve the steady-state and dynamic performance of the PMSM drive system. Full article

An important aspect of the application of unmanned systems is the reliability and safety of controlling these devices. An innovative approach has been proposed to improve the efficiency of the pilot operator and the reliability of the automated control system. It includes the development of an algorithm for determining the pilot’s condition based on heart rate analysis. This algorithm helps to assess the condition of the pilot and his ability to control the drone. Another important element of the proposed approach is the algorithm for selecting the control mode of the automated control system for unmanned aerial vehicles, which takes into account information about the functional state of the pilot operator. This algorithm allows the system to automatically switch between different operating modes depending on the condition of the pilot, ensuring optimal control and minimizing the risks of human error. An integrated approach to improving the reliability of the unmanned aerial vehicle control system allows not only improving the work of the pilot operator, but also ensuring the safer and more efficient operation of automated systems. Full article

In recent years, the pulse-width-modulation (PWM) converter has been found to have extensive applications in renewable energy, industrial fields, and others. The high efficiency requirement is crucial to operating a PWM rectifier in various applications, in addition to the fundamental control objectives of sinusoidal grid currents and the correct DC bus voltage. Additionally, in practical application, another issue arises when the grid voltage frequently experiences distortion, leading to a distorted grid current and a significant rise in total harmonic distortion (THD). To resolve these problems, a model predictive virtual flux-based direct power control (MPVFDPC) with improved power loss performance is proposed based on an integrated switching state predetermination strategy. The proposed MPVFDPC for PWM rectifier inherits the merits of both virtual flux control and direct power control, which have fast dynamic performance and the grid current THD is considerably decreased under distorted grid voltage states. The proposed technique aims to minimize switching loss under ideal and distorted grid voltage states by exploiting the discontinuous modulation concept by using a switching state predetermination strategy. The MPVFDPC with switching state predetermination strategy is proven by employing it in experiments as well as simulations in comparison with previous models: predictive direct power control (Conv. MPDPC) and conventional MPVFDPC (Conv. MPVFDPC). The acquired waveforms and quantitative data are employed to prove the effectiveness of the developed algorithm. Full article

Path planning is an indispensable component in guiding unmanned ground vehicles (UGVs) from their initial positions to designated destinations, aiming to determine trajectories that are either optimal or near-optimal. While conventional path-planning techniques have been employed for this purpose, planners utilizing reinforcement learning (RL) exhibit superior adaptability within exceedingly complex and dynamic environments. Nevertheless, existing RL-based path planners encounter several shortcomings, notably, redundant map representations, inadequate feature extraction, and limited adaptiveness across diverse environments. In response to these challenges, this paper proposes an innovative and highly self-adaptive path-planning approach based on Transformer encoder feature extraction coupled with incremental reinforcement learning (IRL). Initially, an autoencoder is utilized to compress redundant map representations, providing the planner with sufficient environmental data while minimizing dimensional complexity. Subsequently, the Transformer encoder, renowned for its capacity to analyze global long-range dependencies, is employed to capture intricate correlations among UGV statuses at continuous intervals. Finally, IRL is harnessed to enhance the path planner’s generalization capabilities, particularly when the trained agent is deployed in environments distinct from its training counterparts. Our empirical findings demonstrate that the proposed method outperforms traditional uniform-sampling-based approaches in terms of execution time, path length, and trajectory smoothness. Furthermore, it exhibits a fivefold increase in adaptivity compared to conventional transfer-learning-based fine-tuning methodologies. Full article

Upper limb functionality is essential to perform activities of daily living. It is critical to investigate neurorehabilitation therapies in order to improve upper limb functionality in post-stroke patients. This paper presents a soft-robotic bilateral system to provide rehabilitation therapy for hand and wrist joints. A sensorized glove that tracks finger and wrist joint movements is worn on the healthy limb, which guides the movement of the paretic limb. The input of sensors from the healthy limb is provided to the soft robotic exoskeleton attached to the paretic limb to mimic the motion. A proportional derivative flow-based control algorithm is used to perform bilateral therapy. To test the feasibility of the developed system, two different applications are performed experimentally: (1) Wrist exercise with a dumbbell, and (2) Object pick-and-place task. The initial tests of the developed system verified its capability to perform bilateral therapy. Full article

The lateral thrust device is a typical high-pressure sealed cavity structure with dual O-rings. Because the O-ring is easily damaged during the assembly process, the product quality is unqualified. To achieve high-precision assembly for this structure, this paper proposes a reinforcement learning assembly research method based on O-ring simulation. First, a simulation study of the damage mechanism during O-ring assembly is conducted using finite element software to obtain damage data under different deformation conditions. Secondly, deep reinforcement learning is used to plan the assembly path, resulting in high-precision assembly paths for the inner and outer cylinder under different initial poses. Experimental results demonstrate that the above method not only effectively solves the problem that the O-ring is easily damaged but also provides a novel, efficient, and practical assembly technique for similar high-precision assemblies. Full article

A novel time-domain algorithm is proposed in this paper for the iterative estimation of drive files. A drive file is a synchronized batch of dynamic time series commands that are simultaneously sent to one or more actuators in a test rig that is designed for service environment replication (SER). When drive file commands are input to an SER test rig, the response of the article under test is similar to what was measured in a service environment. The proposed Pulse Train Filtered-X Least Mean Square (PT-Fx-LMS) algorithm is based on methods developed for active noise and vibration control (ANVC). A time-domain PT-Fx-LMS algorithm is shown through several simulation studies to rapidly converge to a dynamic solution in a small number of iterations for a one degree-of-freedom nonlinear suspension. The PT-Fx-LMS algorithm is also shown to enable targeted iteration over isolated time slices within the data set, which challenges conventional frequency-domain techniques. Full article