Abstract:To address the vibration control problem in helicopter dynamic flight scenarios with continuously varying advance ratios (variable-speed forward flight), a robust vibration control method based on a linear parameter varying (LPV) model was proposed using active trailing-edge flaps. First, a linear time-invariant (LTI) model of the helicopter vibration response was identified using CAMRAD II simulation data, which served as the foundation for developing an LPV model representing the dynamic flight process. Based on this LPV model, an integral sliding mode robust control algorithm was designed. The stability of the control system was proved using a parameter-dependent Lyapunov function. Two distinct helicopter dynamic flight scenarios were designed to simulate and verify the proposed algorithm, which was also compared with the traditional H∞control method commonly used in helicopter vibration suppression. Furthermore, to validate the effectiveness of the proposed algorithm, it was implemented on a semi-physical simulation platform based on LabVIEW. Simulation results demonstrated that the designed integral sliding mode robust control algorithm could suppress vertical vibratory loads on the rotor hub by more than 95% within 2.5 seconds under steady-state conditions and by more than 90% under external disturbances and uncertainties in the process of varying advance ratios. This indicates the algorithm’s ability to effectively suppress rotor hub vertical vibration loads in dynamic flight scenarios.

Abstract:To achieve the escape of a hypersonic vehicle from an interceptor in a multi-role game scenario of “target-interceptor-defender”, it is necessary to execute a cooperative maneuver strategy with the defender. However, due to the limitations of the detection device, hypersonic vehicles face the problem of cooperative maneuver decision-making with imperfect, incomplete, and intermittent strong information constraints. To address this, this paper proposed an end-to-end cooperative maneuver decision-making approach by integrating a multi-agent deep reinforcement learning algorithm, enabling hypersonic vehicles to make cooperative maneuver decisions under strong information constraints and achieve successful evasion. First, the research scenario was modeled as a decentralized partially observable Markov decision process, and an observation information sharing stacking mechanism was proposed for the design of local observation state spaces under the strong information constraints. Second, to address the sparse reward problem in multi-agent deep reinforcement learning, a cooperative decision-making reward function was constructed by integrating game relationships and zero-effort miss distance, enhancing training efficiency in complex game scenarios. Finally, a multi-agent cooperative decision-making network architecture was designed, comprising the agents basic networks and the top value decomposition network. This architecture extracted spatio-temporal trajectory features from imperfect, incomplete, and intermittent information, enabling policy coordination among agents and cooperative maneuver decision-making for vehicles. Research results demonstrate that hypersonic vehicles equipped with the proposed intelligent cooperative maneuver decision-making approach can successfully evade in multi-role game scenarios under strong information constraints. The proposed approach exhibits outstanding performance and robustness in numerical simulations, including typical game scenarios and Monte Carlo tests.

Abstract:To accurately identify the inertia tensor parameters of flexible solar sail spacecraft during on-orbit deployment and improve the accuracy and robustness of the parameter identification model in complex disturbance environments, this paper proposed a data-driven parameter identification method based on a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) hybrid network. This method combined the local feature extraction capability of CNN with the time series data modeling advantage of BiLSTM. First, the paper established the attitude-vibration coupling dynamics model of the flexible solar sail spacecraft and generated massive training data using domain randomization. Second, a CNN-BiLSTM network parameter identification model was constructed and trained using a pre-designed training strategy. Finally, the trained model was applied to identify the inertia tensor of the solar sail under different measurement noise conditions, and the identification results of this model and single models were compared and analyzed. Simulation results demonstrate that the constructed network model has a mean relative error of less than 1% in identifying the system inertia tensor parameters under disturbance-free conditions. In addition, under complex measurement noise conditions with disturbance torques and lower signal-to-noise ratios, the mean relative error of the identification results still does not exceed 1.5%, which is significantly better than that of the CNN and BiLSTM models. The proposed method effectively addresses the shortcomings of single network models, not only accurately identifying the time-varying inertia tensor of the flexible solar sail but also enhancing robustness in complex disturbance environments.

Abstract:This paper aims to investigate the patterns and formation mechanisms of localized jump phenomena in aerodynamic characteristics before and after adding tail supports in the CHN-T2 wind tunnel experiments, thereby providing references for the selection of tail support structures and support interference correction of experimental data in subsequent similar wind tunnel experiments. The numerical simulation software NNW-FSI, developed with funding from national numerical wind tunnel project, was employed to conduct numerical simulation research on the influence of tail supports on local flow structures for the CHN-T2 wing/body/horizontal tail/vertical tail combination configuration (under computational conditions of Mach number of 0.85 and angle of attack ranging from -5° to 10°). First, the reliability of the computational data was verified by conducting numerical simulations of the CHN-T2 model and comparing the calculated aerodynamic data with experimental data. Subsequently, based on computational data with and without tail support, the patterns of tail support’s influence on the aerodynamic characteristics of the CHN-T2 model were analyzed, and the angle-of-attack range where a jump occurred in the aerodynamic characteristics was identified. Finally, a detailed analysis was performed on the results at a 5° angle of attack, including local streamlines and separation vortices in the wing-body junction region, spanwise sectional pressure distributions of the wing, local flow structures near the tail, and the patterns and mechanisms of tail supports influence on tail pressure distribution. The analysis results show that within specific flight Mach numbers and angle-of-attack ranges, the tail support induces localized flow interference near the tail, which propagates forward, increasing pressure at the trailing edge of the wing root, suppressing flow separation there, and eliminating the separation vortex in the wing-body junction region of CHN-T2. Consequently, these effects alter the aerodynamic characteristics of the CHN-T2 model.

Abstract:To investigate the horizontal tail buffet flow characteristics and mechanisms for civil aircraft at low speeds and high angles of attack (AOA), this study conducted high-precision flow simulations for a typical civil aircraft configuration under flow separation conditions based on the stress-blended eddy simulation (SBES) method. Firstly, the accuracy of the numerical simulation in resolving separated flow is validated using a NACA 0015 airfoil at high AOAs. Then, numerical simulations were performed for a clean configuration of a civil aircraft and compared with wind tunnel test data. The comparative analysis shows that the numerically predicted mean and fluctuating pressure coefficients on the horizontal tail agree well with the wind tunnel test data under various flow conditions and exhibit consistent variation trends with AOA, thus verifying the methods suitability for simulating the horizontal tail flow field under the interference of wing separation flow. Finally, based on the comparison between the simulation results and experimental data, the mechanism and evolution of aerodynamic interference on the horizontal tail caused by wing separation flow are analyzed in depth. Analysis results reveal that near the buffet boundary, local wing flow separation induces only minor buffet loads on the horizontal tail. As AOA increases, strong wing wake separation vortices gradually dominate the flow field of the horizontal tail, significantly amplifying the buffet loads of the horizontal tail. As the AOA increases further, the horizontal tail gradually moves out of the wing wake region, causing the growth of the buffet load amplitude to slow down. Consequently, the flow field becomes dominated by the horizontal tails own high-AOA separation flow, accompanied by a shift of the dominant flow frequency toward higher frequencies. The study clarifies the evolution of buffet load characteristics of the horizontal tail under the influence of wing separation flow at high AOA, offering guidance for safety assessment and design of civil aircraft.

Abstract:To achieve damage assessment of solar arrays with large exposed areas under micro-meteoroid & space debris (MM/SD) impacts, this paper proposed a damage assessment method for solar arrays, focusing on submillimeter MM/SD impacts. This method considered the uncertainty of impacts and the cumulative damage effects. The effectiveness of the solar array impact frequency model was validated through comparison with NASA detection data. The study focused on solar arrays in three orbits with altitudes of 400 km, 800 km, and 35 787 km. First, the flux-size-velocity distributions of MM/SD on spacecraft orbits were determined using the space debris environment engineering model and micrometeoroid models. Then, MM/SD data were generated based on these distributions, and random impact points were selected for simulated impact experiments, achieving visualization of cumulative effects. Next, based on the mechanical damage characteristics and volt-ampere characteristics of the solar array, the impact damage features and power loss were analyzed. Finally, with power loss as a protective indicator, the effect of glass cover thickness on protection performance was studied. The simulation results show that the proposed method can effectively assess impact damage to solar arrays on different orbits, accurately characterize damage features, and reflect the differences in impact damage caused by MM/SD of varying sizes, providing references for on-orbit mission damage assessment and protective structure design for spacecraft.

Abstract:To improve the anti-interference ability and control accuracy of wave compensation control for parallel robots, and to address the problem of insufficient robustness of existing methods in complex marine environments, this study proposes a novel TNL function observer and a T-S fuzzy control strategy for a 3-RSR parallel robot. Firstly, a kinematic model incorporating limb constraints is constructed based on the kinematic characteristics of the mechanism, and the Jacobi matrices between the joint and operational spaces are deduced; a nonlinear dynamics model is established through the principle of virtual work and the Newton-Euler method to elucidate the coupling mechanism of inertial, Coriolis, and gravitational forces. Secondly, to address the highly nonlinear characteristics of the system, the T-S fuzzy model is adopted to accurately approximate the dynamical equations, thereby realizing local linearization modeling based on membership functions. The TNL function observer is innovatively designed, and the robust stability condition characterized by LMIs is constructed by combining the Lyapunov stability theory to solve the problem of unmeasurable system states. Finally, through a co-design of the observer and fuzzy controller, an anti-disturbance closed-loop system is constructed, and convex optimization is used to solve controller parameters that satisfy the H∞ performance index, thereby guaranteeing dynamic stability under disturbances. Simulations show that the TNL function observer has better performance than the conventional types. The proposed architecture can effectively address the challenges of nonlinear state estimation and robust control, providing an innovative solution for wave compensation in marine engineering equipment, which is of great practical value for enhancing the performance of operations such as ship-to-ship transfer and maritime rescue.

Abstract:To address the challenges of insufficient cross-domain coordination effectiveness, poor robustness, and weak adaptability in modern air-space defense systems, caused by the fragmentation between rigid structural elements and elastic capability elements, thereby enhancing comprehensive effectiveness and survivability/recovery capabilities in complex adversarial environments, this paper innovatively constructs a stiffness-elasticity coupling dynamics based modeling and effectiveness evaluation framework for air-space defense systems. First, breaking through the limitations of traditional linear superposition models, we propose a four-dimensional dynamic stiffness matrix (4D DSM) and its feedback reorganization mechanism to characterize the bidirectional dynamic coupling between operational readiness and system architecture. Second, by establishing a Markov state transition model incorporating asymmetric constraints, the steady-state probability distribution of the system is rigorously derived. Then, based on nonlinear system stability theory, the state transition mechanism of the aerospace defense system from steady-state operation to critical failure is elucidated. A closed-loop management system comprising situational awareness, threshold-based early warning, and parameter optimization is proposed. Finally, rigorous mathematical derivation is employed to prove that the dynamic adjustment mechanism enabled by rigid-elastic coupling ensures an enhancement in the comprehensive effectiveness of the aerospace defense system. Theoretical analysis and scenario-based validation demonstrate that the proposed dynamic stiffness-elasticity adjustment mechanism significantly enhances the systems architectural adaptability. While ensuring a relative stability margin (10%) for the system, it effectively improves comprehensive effectiveness, restores operational readiness, and reduces system failure probability. This research supports early warning and command decision-making for aerospace defense systems, thereby proposing a new theoretical framework for synergistic stiffness-elasticity warfare in intelligent air-space defense systems, achieving stiffness shaping and elasticity modeling of dynamic potential.

Abstract:To predict the remaining life of rolling bearings more accurately, this paper proposed a bearing life prediction method based on the fusion of the multi-strategy hippopotamus algorithm (TOBCHO: adaptive t-distribution, optimal-worst opposite learning, and chaos mapping) and bi-directional gated recurrent unit (BiGRU). Firstly, feature extraction was performed for the whole life cycle signal, and comprehensive evaluation indexes were established based on correlation, monotonicity, and robustness. Sensitive feature vectors were screened as the sensitive feature set, and principal component analysis (PCA) technology was used to construct the health index curve. Then, for the problem that it was difficult for BiGRU to determine the hyperparameters in rolling bearing life prediction research, a life prediction model of TOBCHO-optimized BiGRU (TOBCHO-BiGRU) was proposed, which introduced the optimal worst opposition-based learning mechanism in the population initialization stage of the hippopotamus algorithm and generated the opposing solution to expand the search space. The chaos mapping sequence was adopted to replace the generation of random numbers to solve the problem of unstable convergence of the algorithm. The adaptive distribution perturbation strategy for optimal individual was introduced in the late iteration stage of the hippopotamus optimization (HO) algorithm, and the perturbation strength was dynamically adjusted to balance the local development and global search capability. Finally, the experimental validation was conducted on the internationally used IEEE PHM 2012 bearing dataset, and the proposed model was compared with a variety of other prediction models. The results adequately show that the proposed TOBCHO-BiGRU method has a significant advantage in terms of prediction accuracy. Ablation experiment results demonstrate that there are positive synergistic effects among improvement strategies, promoting the enhancement of the HO algorithm, which provides a high-precision solution for the rolling bearing life prediction under complex working conditions.

Abstract:To address the low plume tracking efficiency and excessively long search paths caused by the inability of robots to obtain reliable wind information, such as plume flow direction and flow velocity, in indoor plume diffusion environments, this paper proposed an autonomous plume tracking method for indoor robots based on an improved sparrow search algorithm (SSA). Firstly, inspired by the predation and anti-predation behavior of the sparrow population, this method used plume concentration as the fitness value of individuals so that the source-seeking robot could efficiently track the plume and locate the source position when it was not equipped with a plume flow direction sensor and a plume flow velocity sensor. Secondly, Logistic chaotic mapping was used to disperse the initial position of the sparrow population, and the elite solution of the sparrow population was retained to increase the population diversity. The Metropolis criterion was added to increase the probability of the algorithm escaping from the local extreme value area when updating the optimal solution. The improved A* algorithm was combined to optimize the search path. Finally, a plume tracking simulation comparison experiment was conducted, in which the improved sparrow search algorithm (ISSA) was compared with the genetic algorithm (GA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), and classic SSA, further validating the feasibility and effectiveness of the proposed algorithm in physical scenarios. The results show that compared with the aforementioned methods, the success rates of the proposed method increase by 31.00%, 4.84%, 1.34%, and 13.34%, respectively, and the search path lengths are shortened by 12.8,6.2,4.941, and 5.448 m, respectively. This study provides a new approach and reference for enabling efficient plume tracking by mobile robots in plume diffusion environments where reliable wind information is unavailable.

Abstract:To address the issues of unstable output voltage and relatively low transfer efficiency in magnetically coupled wireless power transfer (WPT) systems caused by dynamic load variations, coupling coefficient fluctuations, and component parameter drift in practical applications, this paper proposed a dual-side phase-shift cooperative control strategy based on a radial basis function neural network (RBFNN) and an improved perturb and observe (P&O) algorithm. First, this paper constructed and analyzed a mathematical model of the WPT system with a bilateral LCC compensation topology. To address the nonlinearity and uncertainties of the model, this paper designed an RBFNN controller with online self-learning capability. By collecting real-time system output error information to dynamically adjust network weights, it directly generated accurate phase-shift angle control signals and regulated the system output voltage by controlling the phase-shift angle of the controllable rectifier circuit at the receiving end. This effectively overcame the shortcomings of traditional control methods relying on precise models and poor adaptability. Second, to maximize the transfer efficiency while ensuring stable output voltage, the transmitting end adopted a variable-step P&O algorithm to dynamically adjust the phase-shift angle of the inverter circuit for maximum efficiency tracking. Finally, this paper built an experimental prototype for verification. The results demonstrate that the system output voltage exhibits excellent dynamic response performance and can achieve overshoot-free tracking of the desired voltage; it shows strong robustness under disturbances, with a voltage fluctuation range of less than 1%; meanwhile, the system efficiency increases by a maximum of 14.9%, which fully proves the effectiveness of the proposed method.

Abstract:The transient stability of power systems is crucial for ensuring secure grid operation and continuous power supply, and accurately identifying the transient dominant instability mode (DIM) of power systems is key to formulating effective emergency control strategies. To address the problem of imbalanced distribution of power system transient characteristic data, this paper proposed a two-stage decoupling learning and multi-teacher distillation (TSDM) framework. This framework employed a two-stage decoupling training strategy to achieve the collaborative optimization of representation learning and classifier training. First, instance sampling was used to train multiple teacher models to learn the global feature distribution of the power system transient characteristic data. Second, class-balanced sampling was adopted to train the student model, which transferred high-order feature representations from the teacher models through feature distillation rather than directly reusing their classifier weights, thereby mitigating the problem of bias propagation. Simultaneously, normalization was applied to the feature vectors and classifier weights, respectively, effectively eliminating the prediction biases caused by differences in feature scales. Finally, a separable Transformer module served as the backbone network; through a parameter sharing mechanism and attention optimization design, this module could accurately capture the spatiotemporal correlation features of long time sequences, ensuring that the feature extraction performance was not affected by sequence length. Simulation results based on the CEPRI-36 node system case show that the proposed method achieves a classification accuracy of 98.61% in the recognition of DIM of power systems, particularly demonstrating a significant advantage in the recognition rate of minority class samples, and it provides an effective solution for power system transient stability analysis.

Abstract:To accurately describe the dynamic characteristics of grid-forming direct-drive wind turbines and analyze their stability under different grid strengths, this study considers the source-side characteristics of wind turbines and conducts small-signal modeling of the system. First, based on the state-space modeling method, a comprehensive small-signal model of the entire grid-forming direct-drive wind turbine system with virtual synchronous generator (VSG) control is constructed, and its accuracy is verified through simulation tests. Second, eigenvalue-based modal analysis is employed to investigate the impact of different grid strengths on system stability and assess its adaptability in weak grid environments. Additionally, the mechanism of low-frequency oscillations induced by wind speed step disturbances is analyzed, along with the effects of system structural parameters (such as DC-side capacitance) and controller parameters on the systems dynamic performance. Finally, an RT-LAB hardware-in-the-loop simulation platform is used to build the system model, validating its response capability and accuracy in real-time simulation environments. The results indicate that grid-forming direct-drive wind turbines exhibit strong adaptability in weak grid conditions but may prompt low-frequency oscillations under wind speed step disturbances. Moreover, properly adjusting system structural parameters and optimizing controller parameters can effectively suppress low-frequency oscillations and enhance system stability in weak grid environments. This study provides a systematic modeling approach for analyzing the dynamic characteristics of grid-forming wind turbines and offers theoretical support for further optimizing their stability control strategies.

Abstract:To address the problems of insufficient real-time performance, high computational resource consumption, and low detection accuracy in practical environments for substation defect detection tasks, this paper proposed a lightweight object detection model, namely ELT-RTDETR. First, EfficientFormerV2 was adopted as the backbone network, combining local convolutions with a lightweight Transformer design to significantly reduce the number of model parameters and computational overhead. Second, a lightweight multi-scale feature pyramid network (LMSFPN) was proposed to enhance the expression capability of multi-scale defect features through multi-scale depth-wise convolutions, weighted fusion, and efficient upsampling strategies, while reducing redundant computations. Finally, a token statistics self-attention cross-feature enhancement (TSSACFE) module based on the token statistics self-attention (TSSA) mechanism was introduced. This module optimized feature interaction through local statistical modeling and low-dimensional projection, effectively improving the detection robustness of small defects. Results show that on a self-built substation equipment defect dataset, the detection accuracy of ELT-RTDETR reaches 82.1%, which is 7.3% higher than that of the traditional RT-DETR. Meanwhile, the model calculation volume and parameter count are reduced by 63.2% and 50.7%, respectively. Ablation experiments and comparisons with mainstream algorithms demonstrate that the proposed model outperforms the YOLO series and existing RT-DETR variants in terms of accuracy, light weight, and inference efficiency, especially in tasks such as meter shell damage and silica gel canister discoloration. This study provides an efficient solution for real-time defect detection in substation environments, possessing significant potential for engineering applications.

Abstract:To improve the resource scheduling efficiency and battlefield adaptability of distributed defense systems in complex adversarial environments and to solve the problem that traditional characterization methods mainly focus on local capabilities and are difficult to finely quantify the global spatiotemporal distribution characteristics of resources, this paper introduced spatial density evaluation into the effectiveness analysis of distributed defense and proposed a new characterization method for distributed defense capability based on kill web density. First, this paper proposed the concept of kill web density to quantify the spatiotemporal distribution characteristics of resources. On this basis, combined with threat weighting and gridded spatial partitioning, this paper proposed a new characterization method for distributed defense capability based on kill web density. Secondly, by systematically defining the theoretical model of kill web density and combining the multi-dimensional constraints of command and control centers, tracking radars, guidance radars, and launch vehicles, this paper constructed a dynamic density evaluation framework based on polar coordinate system partitioning to quantify the spatiotemporal distribution characteristics of effective kill chains within each unit and proposed a relative range index of partition density to achieve the refined evaluation of resource deployment balance. Finally, the effectiveness of the analysis method was verified through the simulation of typical attack-defense scenarios. The results indicate that the proposed method can effectively distinguish between threat-driven resource bias and the strengths and weaknesses existing in passive deployment, can provide an evaluation basis for the optimal deployment and adaptive scheduling of distributed defense resources, and enhances the adaptability and high efficiency of distributed defense in actual combat.

Abstract:To address the demands of modern wireless communication systems for radio frequency (RF) power amplifiers (PAs) with high power output, broadband high efficiency, and miniaturization, this study proposes a novel design methodology for broadband high-efficiency PAs suitable for high-power output in the P/L/S bands, with a particular focus on the L band. Firstly, based on the extension of the high-efficiency impedance matching region of continuous inverse Class-F PAs and the impedance characteristics of fundamental low-pass matching circuits, a quasi-continuous inverse Class-F PA impedance model and the corresponding output circuit topology are developed. Secondly, by effectively utilizing the parasitic parameters of large-size transistors in high-power PAs and reusing fundamental and harmonic matching circuit components, the design achieves a compact high-efficiency configuration with minimal components across a wide frequency band, significantly reducing circuit complexity. Finally, to validate the proposed methodology, a prototype PA was designed using a gallium nitride high electron mobility transistor (GaN HEMT) as the primary device, targeting the 1.31.9 GHz frequency band. Experimental results demonstrate a saturated output power of 46.0146.73 dBm and a power gain of 24.0124.73 dB. Moreover, the PA achieves high-efficiency performance, with a power-added efficiency (PAE) of 65.1%69.6% and a drain efficiency (DE) of 74.1%79.5%. Compared with domestic and international PA designs within similar frequency ranges, the proposed approach ensures broadband high efficiency while significantly reducing circuit size and enhancing structural compactness. This design offers a promising solution for miniaturized, broadband, high-efficiency PAs with potential applications in wireless communication, radar systems, and related fields.

Abstract:To meet the requirement of smooth continuity during the deformation process of the shear-type variable-sweep wing, this paper proposed a flexible skin structure using pre-stretched silicone rubber as the shape-maintaining surface layer. To ensure that the skin maintains smooth continuity within the variation range of the wing sweep angle, this paper investigated the critical shear angle for skin smoothness and wrinkling. First, based on a three-parameter solid model characterizing creep behavior and a dimensionless normalization analysis method, this paper established a viscoelasticity prediction model of the silicone rubber surface layer. Second, according to the minimum stress criterion, this paper established a critical model considering the viscoelasticity of the silicone rubber surface layer, completed the calculation of the critical shear angle of the pre-stretched flexible skin, and proposed an empirical formula for solving the critical shear angle. Finally, this paper simulated and analyzed the critical shear angles of skins with different pre-stretching amounts using the finite element method, generated tensile deformation by applying displacements along the x and y directions, and generated shear deformation by applying rotation and displacement along the diagonal direction. The results indicate that within a large tensile strain range, the viscoelastic model of the silicone rubber surface layer can accurately describe the static creep behavior of the silicone rubber, and the critical shear angle model for the smoothness of the pre-stretched flexible skin considering the viscoelasticity of the silicone rubber surface layer has high accuracy; the pre-stretching amount and the creep characteristics of the silicone rubber surface layer have a significant impact on the critical shear angle, and biaxial stretching is beneficial to improve the anti-wrinkling ability of the skin, thereby maintaining surface smoothness during the variable-sweep process. The theoretical prediction results agree well with the simulation results and experimental results, which has great value for the engineering application of the shear-type variable-sweep wing.

Abstract:To improve the transient performance of an axial-flow propulsion pump under rapid start-up conditions and address the drop of head in the saddle region under complex operating environments, this paper proposed a control method for imported inclined groove flow, aiming to enhance the steady-state and transient performance of the pump by optimizing the inflow conditions. Firstly, numerical simulation calculation was conducted on the performance of the axial-flow propulsion pump, and the accuracy of the numerical calculation was verified through experiments. Secondly, the sizes of the inclined groove were designed, and numerical calculations were conducted to investigate the influence of the imported inclined groove on the transient performance of the axial-flow propulsion pump during rapid start-up. Finally, internal flow field analysis was performed to investigate the mechanism of inclined grooves for improving the transient performance of the propulsion pump. The results demonstrate that the inclined groove effectively improves steady-state performance in the saddle zone, achieving a maximum head increase of 56.5%. The rapid start-up process is categorized into four stages based on head evolution: primary development region, saddle region, secondary development region, and stable operation region. The inclined groove improves the drop of head during start-up, with a maximum increase of 27.15%. Localized low-pressure zones in the upstream and downstream of the inclined groove induce reverse flow inside the inclined groove, which is mixed with the main flow to reduce circumferential angular momentum. The optimized inflow conditions improve flow separation, suppress the tip blockage vortex, and reduce pressure fluctuation amplitudes in the rotor-stator interaction zone, thereby enhancing the stability during rapid start-up.

Abstract:To achieve a more accurate load distribution for ball screw pair with internal circulation and optimize structural parameters of ball screw pair to enhance load-bearing performance, this study established a novel load distribution computation model. Building upon the traditional continuous load distribution model, the proposed approach incorporated the unloaded zone induced by reversers. First, ball-nut and ball-screw contact models were established. Based on the deformation compatibility mechanism between balls and raceways, the continuous load distribution model was derived. Second, the spatial position distribution of the ball chain was determined according to the ball return curve. On this basis, a load distribution solution method for ball screw pair considering balls in unloaded zones was proposed. By using this method, quantitative comparisons were conducted between the proposed model and the traditional continuous load distribution model regarding full-ball loads and contact stresses. The effects of the number of reversers and different ball diameters on key load-bearing characteristics, such as contact loads and static stiffness, were thoroughly analyzed. Finally, static loading experiments were conducted to validate the accuracy of the proposed load distribution model. Experimental results show that the maximum error in axial contact deformation between the proposed model and experimental measurements is 5.42%, and the maximum error in static stiffness between them is 6.69%, confirming the proposed model’s accuracy. Compared to the traditional model, the proposed model exhibits higher agreement with experimental results, providing a method for the precise design of ball screw pair.

Abstract:Cells are the mysterious source of life and the fundamental unit of all life activities in living organisms. The primary challenge in constructing a living artificial system lies in replicating the original form of life, namely, the cell. In order to create a system of cells with vitality, this paper innovatively proposed a design strategy. Specifically, by employing magnetic field programming to control clusters of magnetic particles encapsulated within droplets at gas/liquid interfaces, artificial cells with vitality were successfully fabricated. Furthermore, the paper conducted a detailed analysis of the underlying mechanisms driving their bionic behaviors, such as predation and parasitism. Firstly, by regulating the matrix droplets and magnetic field parameters of artificial cells, the basic life activities of cells in nature were reproduced, such as migration, predation, and parasitism. Then, according to the influence of the concentration, size, and liquid types of droplets on the behavior patterns of artificial cells, the paper revealed the underlying mechanism of the bionic behaviors of artificial cells. Finally, based on the predation and parasitism capabilities learned from the artificial cells, the paper designed and carried out a series of experiments on water surface pollutant removal and microchemical reactions. The results indicate that the artificial cell system presented here is of good stability and controllability, and it can achieve various bionic behaviors of cells. Moreover, it is expected to provide potential solutions for fields such as medical diagnosis and treatment, reagent testing, and environmental protection.

Abstract:To enhance the performance and adaptability of trajectory tracking predictive control for omnidirectional mobile robots in dynamic environments and address the limitations of existing machine learning-based parameter tuning methods, such as strong data dependency and the difficulty in balancing short-term control accuracy with long-term system performance, this paper proposed an online self-tuning method for model predictive control (MPC) parameters. This method integrated reinforcement learning theory with an event-triggered mechanism. First, the kinematic model of the omnidirectional mobile robot was established, and a corresponding trajectory tracking MPC framework was constructed. Second, a dynamic parameter optimization framework incorporating the Actor-Critic reinforcement learning was introduced. By designing a reward function that combines state errors and dynamic performance metrics, the controller was driven to optimize control parameters in real time. Furthermore, the event-triggered mechanism was seamlessly integrated into the parameter optimization framework to develop an adaptive controller. This integration reduced the frequency of parameter updates, thereby lowering computational load and enabling efficient control. Finally, a physical experimental platform for omnidirectional mobile robots was developed, and comparative experiments were conducted across multiple scenarios, including step trajectory tracking, Lemniscate curve tracking, and dynamic obstacle avoidance. Experimental results demonstrate that compared to traditional MPC methods using static parameters, the proposed approach reduces overshoot and adjustment time by approximately 70% in step trajectory tracking, decreases state deviation by approximately 65% in Lemniscate trajectory tracking, and reduces state deviation by approximately 30% in dynamic obstacle avoidance scenarios. These results validate the effectiveness and environmental adaptability of the proposed method in enhancing trajectory tracking performance in complex dynamic environments. This research provides novel insights and approaches for addressing the challenges of high-performance trajectory tracking control of mobile robots in dynamic and uncertain conditions.

Abstract:To address the issues of poor assistance effect, heavy weight, and insufficient human-machine compatibility in existing passive lower limb assistive robots, this paper proposed a passive lower limb assistive robot with better assistance effect, lighter weight, and improved human-machine compatibility. Firstly, this paper conducted inverse kinematics analysis of the human lower limbs and constructed a dynamic model of the human lower limbs using the Lagrange equation to provide a biomechanical basis for the robot design. Secondly, the paper designed the mechanical structure by combining the biological movement characteristics of the lower limbs. Specifically, the hip joint adopted a three-degree-of-freedom structure to enhance human-machine compatibility, and the leg structure employed a hollow design to reduce the self-weight. Then, the paper established a human-machine fusion model in OpenSim for simulation experiments to verify the assistance effect of the passive lower limb assistive robot. The results show that compared with those in the unassisted state, the average torques of the hip, knee, and ankle joints after wearing the robot decrease by 19.64%, 24.85%, and 15.39%, respectively; the average total metabolism of the human body decreases by 16.35%. This significantly reduces the energy consumption of the wearer and effectively alleviates muscle burden. Finally, the paper develops a prototype of the passive lower limb assistive robot and conducts prototype experiments. The experimental results indicate that compared with that in the unassisted state, the root mean square of the reduction rate of electromyographic (EMG) signals of the main lower limb muscles ranges from 5.73% to 13.79% after wearing the robot. The experimental results confirm the advantages of the designed passive lower limb assistive robot in terms of light weight, high compatibility, and multi-scenario adaptability, laying the foundation for future optimization of the prototype.