A planar prismatic Rubik's cube mechanism (PPRCM) is defined as a Rubik's-cube-inspired mechanism whose rigid components are constrained to pure planar movements. Taking a typical PPRCM prototype as the research carrier, this paper elaborates on the composition, topological configuration, and reconfigurable motion process of the mechanism. The influencing factors of PPRCM's topological structure are abstracted, and the conditions for realizing reconfigurable motion are thereby derived. Based on the reconfigurable topological characteristics of PPRCM, a type synthesis method for the mechanism is proposed. By specifying the values of the topological influencing factors, PPRCMs with different topological structures are generated, and corresponding three-dimensional(3D) models are constructed to validate the rationality and effectiveness of the proposed method. The type synthesis method for PPRCM proposed in this paper not only enables the development of more novel PPRCM configurations but also enriches and improves the type synthesis theories for planar mechanisms.
This paper proposes a composite control strategy combining a radial basis function (RBF) neural network and super-twisting sliding-mode control for high-precision trajectory tracking of the UR10 manipulator under parameter variations, nonlinear friction, and external disturbances. An RBF network is employed to approximate the lumped unknown nonlinear dynamics online, thereby reducing the equivalent disturbance upper bounds. Simultaneously, a super-twisting robust control term is introduced to compensate for approximation residuals and remaining disturbances, which ensures system robustness while effectively suppressing conventional sliding-mode chattering. Furthermore, a projection-based adaptive law is designed to guarantee the strict boundedness of the network weights. Based on Lyapunov theory, the finite-time convergence of the sliding variable and tracking error is proven. Simulations on a 6-degree-of-freedom UR10 manipulator – incorporating mass/inertia perturbations and Coulomb-viscous friction – demonstrate that the proposed method achieves high-precision tracking with small steady-state errors and smooth, chattering-free control torques, verifying its effectiveness and engineering applicability.
To address the low efficiency of traditional geometry-based step error calculations in five-axis numerical control (NC) finishing that cannot reuse historical data, this study proposes a step error prediction method based on a SSA-Attention-LSTM (Sparrow search algorithm–attention mechanism–long short-term memory) model. The method employs principal component analysis (PCA) for dimensionality reduction, optimizes LSTM hyperparameters using the Sparrow search algorithm, incorporates an attention mechanism to enhance key feature extraction, and introduces dropout to prevent overfitting. Experimental results on two surface models show that the proposed model achieves an average R2 of 99.4 %, which is 31 %, 10 %, 5 %, and 6 % higher than that of back propagation (BP), long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and bidirectional long short-term memory (BiLSTM) models. In the blade test case, the model achieves high-precision prediction using only 9480 training samples, with an R2 of 0.995, demonstrating high prediction accuracy for step error in five-axis finishing tool paths.
A two-wheeled self-balancing wheelchair is a highly nonlinear and underactuated wheeled inverted-pendulum system, which faces severe balance control challenges under large tilt angles, load variations, and external disturbances. A hybrid nonlinear model predictive control-linear quadratic regulator (NMPC-LQR) balance control scheme is presented that combines nonlinear optimal recovery and efficient local stabilization while reducing the computational burden. A reduced-order nonlinear dynamic model of the wheelchair-occupant system is derived via the Lagrange method. The proposed controller confines LQR to a locally valid region verified by linearization-error analysis, whereas NMPC is employed for large-deviation recovery. A linearization-error analysis is first used to determine the switching-threshold search range, and a normalized five-index criterion identifies 0.100 rad as the optimal nominal threshold. Mass-sensitivity tests further provide an empirical relation for estimating the optimal threshold within the tested mass range. Comparative simulations under initial tilt angles of 35, 40, and 45 degrees show faster convergence than standalone LQR and standalone NMPC controllers. Benchmark comparisons with SMC demonstrate a larger recoverable initial-angle range. Robustness tests under parameter variations, pulse and step disturbances, and flat-ground friction changes, together with prototype experiments, validate the stability, disturbance rejection, and engineering applicability of the proposed control strategy.
Mud circulation tanks efficiently remove accumulated mud cake and impurities from the tank, stabilizing drilling fluid performance and directly ensuring drilling safety and efficiency. Automatic cleaning of these tanks is critical for modern drilling operations. This paper designs an integrated optimization system applied to self-excited cavitation jet nozzles to enhance their comprehensive cleaning performance on mud tanks in submerged environments. Using inlet radius, cavity diameter, cavity length, and lower-nozzle diameter as design variables and pressure peak, amplitude, and frequency as objective variables, orthogonal experiments and range analysis were conducted to obtain optimized structural parameters under orthogonal analysis and identify key structural variables. Subsequently, the discrete optimization space, constructed from key structural parameters, was mapped to the continuous optimization space of the machine learning model. A multi-objective particle swarm optimization algorithm was employed to obtain the Pareto front. Finally, the optimal structures were ranked using the technique for order preference by similarity to an ideal solution method. Compared to the orthogonal analysis results, the optimized structure achieved improvements of 69.79 %, 78.60 %, and 11.77 % in pressure peak, amplitude, and frequency, respectively. Overall cleaning capacity increased by 77.39 %. Simulations of the optimal structure obtained through the integrated optimization system revealed that the nozzle's optimal configuration generates a distinct pulsed cavitation jet. The cavitation zone within the nozzle cavity undergoes periodic contraction and expansion over time, validating the optimization results. The self-excited cavitation jet nozzle structure designed via the integrated optimization system significantly enhances its overall cleaning performance, providing an effective solution for the automatic cleaning of mud circulation tanks.
Compliance control is the key to human-robot physical interaction, which can improve the safety and comfort of robot-assisted rehabilitation. Aiming to address the problems of the large time delay and low compliance of the system caused by the slow speed of motion intention recognition based on the force signal, this paper integrates the force signal and the surface electromyography (sEMG) signal as the input of the elbow-wrist rehabilitation robot, which improves the responsiveness of the system. To further enhance the trajectory-tracking performance of the system, a sliding-mode controller based on the double exponential reaching law (DE-SMC) is designed. Then, the force and sEMG signal fusion experiment and the compliance control experiment were carried out. The former showed that the fused force signal would judge the subject's motion intention 102.5 ms in advance, while the latter confirmed that the compliance control strategy based on the DE-SMC controller effectively improved the compliance of elbow and radioulnar joint movement.
With the growing scale, heterogeneity, and dynamic uncertainty of modern supply chain networks, collaborative scheduling across order assignment, manufacturer selection, and logistics operations has become increasingly critical and challenging because of strong inter-stage coupling, high decision complexity, and dynamic operational constraints. To address these challenges, this paper investigates the joint optimization problem of order assignment, heterogeneous manufacturer selection, and logistics vehicle scheduling in dynamic supply chain collaborative networks and proposes a curriculum-learning-driven hierarchical multi-agent deep reinforcement learning framework (CH-MADRL) for coordinated scheduling in complex environments. First, the joint optimization problem is formulated as a hierarchical multi-agent Markov decision process to capture the hierarchical dependencies and dynamic interactions among order assignment, heterogeneous manufacturer selection, and logistics vehicle scheduling, which establishes a unified modeling foundation for multi-stage collaborative scheduling. Second, based on this formulation, a hierarchical multi-agent deep reinforcement learning architecture is developed to decompose the tightly coupled high-dimensional joint scheduling problem into three correlated sub-problems, enabling coordinated optimization across different stages of the supply chain. Third, a constraint-progressive adaptive curriculum-learning mechanism is developed to facilitate policy learning under dynamic constraints, where a stage-conditioned dynamic masking mechanism regulates feasible action spaces, and a dual-gated promotion strategy stabilizes transitions across curriculum stages. Simulation experiments demonstrate that the proposed method surpasses baseline approaches in scheduling performance, training efficiency, and cross-scale generalization capability.
Aircraft assembly is a critical phase in the manufacturing process, where the accuracy and efficiency heavily rely on the performance of posture adjustment mechanisms. For components with point features and linear features, traditional multi-point adjustment technologies based on numerical control positioners are limited by spatial constraints, heavy equipment, and high costs. This paper addresses the challenges of posture adjustment for point-feature and linear-feature aircraft components in confined spaces during assembly (e.g., wing fuselage assembly). We propose a cooperative control strategy using dual-Stewart platforms as core executive units. The approach integrates robust position control with admittance-based force-position coordination to achieve high-precision posture adjustment and internal force-moment suppression. We demonstrate that the system achieves a positioning accuracy of within +/- 0.05mm in translation and +/- 0.05 degrees in orientation through a comprehensive dynamics simulation, with an internal force-moment suppression rate exceeding 90.03 %. The results validate the effectiveness of the method for enhancing flexibility and reliability in aircraft assembly.
The capture of irregular, dimension-variable non-cooperative space debris remains a critical challenge for on-orbit servicing. This paper proposes a continuum gripper based on modified right-angle Miura-ori tessellation, integrating deployable folding and controllable large-range bending. Geometric relations of crease parameters are derived to build a parametric model mapping two-dimensional fold patterns to three-dimensional deployed configurations. An improved Denavit-Hartenberg (D-H) method provides closed-form kinematic solutions, with workspace evaluated via Monte Carlo simulation. A tendon-driven three-finger prototype is tested. Kinematic experiments verify position prediction accuracy and workspace positioning capability. Grasping tests on typical debris simulants confirm passive adaptation and stable enclosure. Load experiments achieve a 265.8 g payload and 100 % grasping success rate, validating the mechanism's controllability and adaptability for on-orbit grasping applications.
Optimizing marine equipment is crucial for enhancing its overall performance, and numerous studies have explored the structural optimization of various marine systems. However, few investigations have focused on the design optimization of submarine cable pallets, despite their importance in marine operations. In this study, a static analysis of a submarine cable pallet under both lifting and transportation conditions was conducted using the finite-element method (FEM). The dimensions of key structural components were sampled using a design of experiment (DOE) approach. The resulting data were utilized to perform a sensitivity analysis on the pallet's performance indicators and to establish a surrogate model. This surrogate model was subsequently combined with a multi-objective particle swarm optimization (MOPSO) algorithm to optimize the pallet design. Specifically, the pallet base and the pallet fence were selected as the optimization targets under lifting and transportation conditions, respectively. The findings reveal significant improvements in the pallet's design, providing a valuable reference for the future structural engineering of submarine cable pallets.
The burrs remaining on switch rails are prone to cracking or even fracturing during operation, thereby diminishing their service life. Moreover, the complex profile of the switch rail makes stable robotic milling difficult, with constantly changing posture and milling force. Therefore, this paper proposes a feed-displacement dual-channel adaptive force control (FDAFC) framework comprising a tangential force-speed loop and a normal-force-displacement loop. The tangential loop employs feed-per-tooth normalization combined with an engagement-aware force-speed mapping and first-order gain scheduling. This design adaptively corrects the feed to regulate tangential force and compensate for engagement-dependent nonlinearities, thereby reducing cross-coupling with the normal channel. The normal loop uses position-based impedance control with radial basis function (RBF)-scheduled inertia, damping, and stiffness to track the desired normal force under time-varying loads. A Lyapunov-guided adaptation law guarantees uniform boundedness, asymptotic tracking-error convergence, and closed-loop stability. Finally, integrated co-simulation and experiments substantiate the efficacy of the compliant milling force-tracking controller for switch rail deburring. The long-distance milling method for robots proposed in this study offers a new approach for automatic burr removal from the switch rail.
When a turboshaft engine operates in a sand-laden environment, it is prone to erosive wear, which leads to the continuous evolution of surface roughness on compressor blades and consequently alters particle impact behaviour. However, existing studies have mainly focused on the erosion process of smooth surfaces, and there is still a lack of in-depth understanding of the influence of surface roughness on the erosive wear of multi-stage compressor blades. To address these issues, this paper theoretically derives the intrinsic relationship between blade surface roughness and wear rate. An erosion experimental setup for titanium alloy with adjustable impact angles is established to accurately characterize key parameters of the erosive wear model for titanium alloys with different surface roughness values. A dynamic model of blade erosive wear based on gas-solid two-phase flow is constructed, and computational fluid dynamics is employed to analyse the effects of sand particles on the distribution characteristics of erosive wear on compressor blades with varying surface roughness. The research reveals that in the erosion wear experiments conducted at impact angles ranging from 0 to 90 degrees, titanium alloys with different surface roughness exhibited the highest wear rate at an impact angle of 30 degrees. At this specific impact angle, the maximum erosion wear depth of the titanium alloy with Ra = 6 & micro;m increased by 88.9% and 183.3% compared to those with Ra = 3 and Ra = 0.1 & micro;m, respectively. Roughness has the most significant impact on erosive wear of rotor blades, followed by stator blades, and the least on guide blades. As roughness increases, the maximum wear rate concentration on the blades rises, while the location of the erosion-concentrated area does not significantly shift with changes in roughness. The results can provide a basis for the erosion wear assessment of compressor blades at different service stages.
Establishing a comprehensive error model that encapsulates all kinematic error parameters constitutes a critical foundation for achieving satisfactory kinematic calibration performance. In this study, a 5-DOF 5PRR+5PUS-PRPU hybrid perfusion mechanism with a variable structure is selected as the research object, and error modeling is conducted using inverse kinematics and the product of exponentials (POE) method, respectively. Comparative analysis of these two error modeling approaches is performed through kinematic calibration simulations of the hybrid mechanism. Firstly, error models of the 5-DOF hybrid perfusion mechanism are established via inverse kinematics and the POE formula method. To replicate real-world kinematic calibration scenarios, the actual kinematic parameters of the mechanism and the actual pose of the moving platform are defined. Owing to the presence of the 5PRR variable base, kinematic calibration simulations of the hybrid perfusion mechanism are executed separately when the base is positioned at different locations. The results demonstrate that, in comparison to the calibration results obtained via the traditional inverse kinematics modeling method, the mean position and orientation errors of the moving platform after kinematic calibration based on the POE error model are reduced by 89.04 % and 63.79 %, respectively. This verifies the correctness and effectiveness of the proposed POE-based error modeling method and kinematic calibration simulation approach, which can be extended to the error analysis of most parallel mechanisms.
Sparse-path Lamb-wave imaging remains challenging because the available pitch-catch paths contribute unequally to defect localisation, and simple equal-weight fusion often produces diffuse and unstable hotspots. This study proposes a reliability-aware physics-guided framework for sparse-path Lamb-wave defect imaging. The method combines unified preprocessing and scattering-envelope extraction, delay-constrained single-path elliptical imaging, two-stage path-reliability weighting, and lightweight image refinement under a soft physics prior. Experimental validation is performed using paired intact-damaged measurements from an aluminium plate with a controlled through-hole defect, which serves as a representative compact scattering source for evaluating the sparse-path imaging chain. Physically aligned scattering-envelope model (SEM)-assisted auxiliary datasets are used for refinement learning and statistical assessment under the same geometry and delay-mapping convention. The results show that single-path imaging is strongly underdetermined, whereas multi-path fusion reduces localisation error from over 100 mm to the order of tens of millimetres. On the SEM-100 benchmark, the trained refinement stage further reduces the mean localisation error from 19.0 to 4.8 mm while substantially improving image quality. The proposed framework therefore provides a practical balance between physical interpretability, sparse sensing, and data-assisted enhancement for guided-wave inspection with limited reliable paths and scarce labelled data.
Under varying flight conditions, helicopter turboshaft engines generate pronounced torque fluctuations that can intensify torsional vibration in the power input chain, accelerating fatigue damage of critical transmission components and threatening flight safety. A lumped-parameter coupled dynamic model of the power turbine and power input chain is developed with time-varying torque excitation. Measured torque signals from three representative flight conditions - hover, high-altitude climb and descent maneuver, and aggressive vertical maneuvers - are applied as inputs to compare torsional responses. Results show that the torque variation rate dominates vibration intensity, as rapid changes promote transient energy accumulation and inertia mismatch within the transmission system. Aggressive vertical maneuvers produce the highest variation rate and the largest torsional response; the torsional angle at the strut-type overrunning clutch is about 10.3 % higher than that in the high-altitude climb and descent maneuver and 280.0 % higher than in hover. These findings clarify the mechanism by which realistic engine torque fluctuations affect torsional vibration and provide theoretical support for the dynamic design and reliability evaluation of helicopter transmission systems.
The rod-type stirred mill is the core equipment used in the grinding of potassium feldspar. However, the grinding process involves numerous parameters that influence performance, various evaluation metrics, and prolonged single-cycle durations, collectively making it difficult to identify optimal operating conditions. To address this challenge, a simulation model of the rod-type stirred mill was developed based on the discrete element method (DEM). This study investigates the effects of rotational speed, grinding media size, and bar spacing on milling performance. By integrating energy efficiency, stirrer wear, collision frequency, and average normal and tangential collision forces, the comprehensive index of milling performance was established. Using Box-Behnken experimental design and analysis of variance, the relative influence of rotational speed, grinding media size, and bar spacing on grinding performance was ranked. With weight coefficients assigned as 0.4 for energy efficiency, 0.2 for stirrer wear, 0.2 for collision frequency, 0.1 for average normal collision force, and 0.1 for average tangential collision force, response surface optimization was conducted. Under the current weighting scheme, the optimal operating parameters of the rod-type stirred mill are determined as follows: a rotational speed of 398 r min(-1), a grinding media size of 6 mm, and bar spacing of 21.2 mm. Under these conditions, the predicted comprehensive grinding performance indicator is 0.781, and the error between the discrete element method (DEM) simulation validation results and the predicted value is only 0.77 %.
In this paper, a novel 4-degree-of-freedom (DOF) parallel mechanism (PM) limb is proposed. The properties of the degrees of freedom are verified based on the screw theory. The inverse position, velocity, and acceleration models of the mechanism are developed. The position workspace of the mechanism is generated based on the inverse kinematic model. The singular configurations are identified within the workspace. The distribution of the stiffness index in the workspace is visualized. The inverse dynamic model of the mechanism is developed based on the Lagrangian method. The kinematic and dynamic simulations of the mechanism are carried out in Adams to verify the correctness of the theoretical model. Most of the kinematic joints of the mechanism are revolute joints and form the sub-closed-loop parallelogram structure, which makes this mechanism exhibit promising application prospects for application in high-speed or heavy-load fields.
This paper presents the design, analytical modeling, and prototype-level experimental assessment of a variable stiffness omnidirectional chain (VSOC) based on positive-pressure fiber jamming. To address the intrinsic pressure limitation (similar to 1 atm) of conventional vacuum-based jamming, an internal inflatable bladder is used to compact a fiber bundle in a rigid chain link, thereby providing a broader tunable pressure range for stiffness modulation. A mechanics-based model is developed for a fiber jamming rod under bending, defining the jamming, transition, and slipping states; deriving the critical shear forces associated with state transitions; and introducing a pressure-dependent equivalent area moment of inertia to describe the variation in stiffness. This framework is then adapted to the two primary bending modes of VSOC. Three-point bending experiments over 0-300 kPa are used to evaluate whether the model can capture the observed pressure-dependent behavior of the present prototype. Effective parameters, including an inter-fiber friction coefficient (mu = 0.3665) and an effective fiber modulus (E = 4.85 GPa), identified from the jamming pressure p = 0 kPa bending response, are used in the present structure-level model. Within the tested pressure range, the results indicate that critical loads and slipping state stiffness increase approximately linearly with jamming pressure, whereas jamming state stiffness is comparatively insensitive to pressure and is primarily governed by geometry. This work bridges design, theory, and experiment to develop a high-performance variable stiffness structure with significant potential for soft robotics and wearable devices.
Traditional autonomous agricultural systems face significant challenges in performing continuous operations within fragmented field regions. To address this issue, it is essential to upgrade these systems to automatically acquire high-precision field boundaries. This study tests the hypothesis that fragmented tobacco parcels can be reliably mapped using a cloud-based, multi-source remote sensing framework and that the resulting products can directly support autonomous field operations. Using Xuchang City, Henan Province, China, as a case study, we developed a cloud-edge-integrated tobacco mapping workflow on the Google Earth Engine (GEE) platform by fusing Sentinel-2 optical imagery, Sentinel-1 synthetic-aperture radar data, and topographic variables. A comprehensive feature set, including spectral bands, vegetation indices, radar backscatter, texture metrics, and terrain attributes, was used to train and compare three machine learning classifiers: random forest (RF), gradient boosting decision tree (GBDT), and classification and regression tree (CART). RF achieved the highest performance, with an overall accuracy of 93.08 % and a kappa coefficient of 0.92, outperforming GBDT (90.60 %, 0.89) and CART (87.60 %, 0.85). The RF-derived tobacco planting area showed the closest agreement with official statistics, with a consistency ratio of 94.12 %. Model robustness was further demonstrated by direct transfer to the adjacent Pingdingshan City without re-training, yielding a 97.70 % consistency with reported acreage. By shifting field-boundary extraction from manual delineation to automated cloud-based processing, this study provides a scalable solution for mapping fragmented tobacco fields and delivering parcel-level geospatial data to autonomous agricultural systems, with broader applicability to other cash crops in fragmented landscapes.
The traditional external fixation has problems such as a single fixed dimension, limited application range, long fixation time, and heavy weight. A single-function external fixation is unable to meet the various needs of fracture fixation in scenarios involving a large number of injured patients. This paper proposes a reconfigurable-configuration comprehensive method based on a seven-link mechanism. A new type of reconfigurable external-fixation frame with multi-dimensional, multi-posture, and multi-fracture scenario applicability is formed, able to achieve rapid and stable external fixation for fractures in the femur, tibia, elbow joint, knee joint, and ankle joint. A reconfigurable mechanism configuration design is proposed based on the comprehensive configuration of the seven-link mechanism and the analysis of the working space. Human-fixator coupled biomechanical model is established, and the mechanical and stability performance of fractures under five reconfigurable configurations are analysed based on finite-element analysis. Compared with the corresponding single-function external-fixation frame in terms of performance, mechanics, and fixation time, a comparative experiment was finally conducted based on the cross-knee joint of dogs for verification. The results confirmed that the external fixation has the advantages of light weight, stable structure, multiple dimensions, multiple postures, and applicability to various fracture scenarios in addition to the fact that it shortens the fixation time significantly (< 10 min). Compared with other unilateral external fixation, the new external fixation is lighter in weight (weighing 950 g), with a fracture displacement of 3 mm, and the load is 274.2 N, the stiffness is 95.2 N mm(-1), and the stability is higher than that of the unilateral external fixator (237.63 N, 72.4 N mm(-1)). When the relative rotation angle of the fracture end reaches 30 degrees, the torque is 14.66 N m. This external fixation can be widely used in temporary fracture fixation in emergency medical scenarios.
