查看更多>>摘要：Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic conditions.This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration.Additionally,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is proposed.The study reveals the inlet model's efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational states.The model's output closely aligns with typical experimental parameters.By combining offline optimization and online adaptive correction,the mechanism-data fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight cycle.Nota-bly,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction.This approach significantly curbs performance deviations in supersonic conditions.For example,at Ma=2.0,the system error impressively drops from 34.17％to merely 6.54％,while errors for other flight conditions consis-tently stay below the 2.95％threshold.These findings underscore the clear superiority of the pro-posed method.
查看更多>>摘要：The active vibration control technology has been successfully applied to several heli-copter types.However,with the increasing of control scale,traditional centralized control algo-rithms are experiencing significant increase of computational complexity and physical implementation challenging.To address this issue,a diffusion collaboration-based distributed Filtered-x Least Mean Square algorithm applied to active vibration control is proposed,drawing inspiration from the concept of data fusion in wireless sensor network.This algorithm distributes the computation load to each node,and constructs the active vibration control network topology of large-scale system by discarding the weak coupling secondary paths between nodes,achieving distributed active vibration control.In order to thoroughly validate the effectiveness and superiority of this algorithm,a helicopter fuselage model is designed as the research object.Firstly,the excellent vibration reduction performance of the proposed algorithm is confirmed through simulations.Sub-sequently,specialized node control units are developed,which utilize STM32 microcontroller as the processing unit.Further,a distributed control system is constructed based on multi-processor col-laboration.Building on this foundation,a large-scale active vibration control experimental plat-form is established.Based on the platform,experiments are carried out,involving the 4-input 4-output system and the 8-input 8-output system.The experimental results demonstrate that under steady-state harmonic excitation,the proposed algorithm not only ensures control effectiveness but also reduces computational complexity by 50％,exhibiting faster convergence speed compared with traditional centralized algorithms.Under time-varying external excitation,the proposed algo-rithm demonstrates rapid tracking of vibration changes,with vibration amplitudes at all controlled points declining by over 94％,proving the strong robustness and adaptive capability of the algo-rithm.
查看更多>>摘要：Accurately evaluating the lifespan of the Printed Circuit Board(PCB)in airborne equip-ment is an essential issue for aircraft design and operation in the marine atmospheric environment.This paper presents a novel evaluation method by fusing Accelerated Degradation Testing(ADT)data,degradation data,and life data of small samples based on the uncertainty degradation process.An uncertain life model of PCB in airborne equipment is constructed by employing the uncertain distribution that considers the accelerated factor of multiple environmental conditions such as tem-perature,humidity,and salinity.In addition,a degradation process model of PCB in airborne equipment is constructed by employing the uncertain process of fusing ADT data and field data,in which the performance characteristics of dynamic cumulative change are included.Based on min-imizing the pth sample moments,an integrated method for parameter estimation of the PCB in air-borne equipment is proposed by fusing the multi-source data of life,degradation,and ADT.An engineering case illustrates the effectiveness and advantage of the proposed method.
查看更多>>摘要：The continuous emergence of new targets in open scenarios leads to a substantial decrease in the performance of Inverse Synthetic Aperture Radar(ISAR)recognition systems.Also,data scarcity further exacerbates the challenge of identifying new classes of ISAR targets.In this paper,a few-shot incremental target recognition framework based on Scattering-Topology Proper-ties(STPIL)is proposed.Specifically,STPIL extracts scattering-topology properties of ISAR tar-gets as recognition features.Meanwhile,the pseudo-incremental training strategy effectively alleviates the algorithm's forgetting of old knowledge,and improves compatibility with new classes.Besides,a feature embedding network,with few parameters,is designed based on the graph neural network.This embedding network is highly adaptable to changes in data distribution.Additionally,STPIL fully considers the joint distribution and marginal distribution in scattering features,and uses the Brownian distance metric module to make the scattering-topology features more discrim-inative.Experimental results on both the simulation dataset and the public measured data indicate that STPIL can effectively balance new classes with old classes,and has superior performance to other advanced methods in the incremental recognition of targets.
查看更多>>摘要：Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar(ISAR)images plays a significant role in space situational awareness and surveillance.However,feature extraction and cross-range scaling of ISAR images create bottlenecks that limit performances of current estimation methods.Especially,the emergence of staring imaging satellites,characterized by complex kinematic behaviors,presents a novel challenge to this task.To address these issues,this article proposes a pointing estimation method based on Convolutional Neural Networks(CNNs)and a numerical optimization algorithm.A satellite's main axis,which is extracted from ISAR images by a proposed Semantic Axis Region Regression Net(SARRN),is chosen for inves-tigation in this article due to its unique structure.Specifically,considering the kinematic character-istic of the staring satellite,an ISAR imaging model is established to bridge the target pointing and the extracted axes.Based on the imaging model,pointing estimation and cross-range scaling can be described as a maximum likelihood estimation problem,and an iterative optimization algorithm modified by using the strategy of random sampling-consistency check and weighted least squares is proposed to solve this problem.Finally,the pointing of targets and the cross-range scaling factors of ISAR images are obtained.Simulation experiments based on actual satellite orbital parameters verify the effectiveness of the proposed method.This work can improve the performance of satellite reconnaissance warning,while accurate cross-range scaling can provide a basis for subsequent data processes such as 3D reconstruction and attitude estimation.
查看更多>>摘要：With cutting-edge technologies and considering airline human-resource-saving,a single pilot in commercial jets could be technically feasible.Investigating changes in captains'natural behaviours are initially required to comprehend the specific safe human performance envelope for safeguarding single-pilot flight,particularly in high-risk situations.This paper investigates how captains'performance transforms for fixing emergencies when operating from Dual-Pilot Operations(DPO)to Single-Pilot Operations(SPO)through a physiological-based approach.Twenty pilots flew an emergency-included flight with/without first officers'assistance.The neural activities and scanning behaviours were recorded using a 32-channel Electroencephalogram(EEG)and glasses-based eye tracker,with the observation and post-experiment questionnaires to evaluate the flight operations and pilots'perception.Flying alone,there was a significantly increased cortical activity in θ and β waves over the frontal,parietal,and temporal lobes during the more complicated emergencies,and pilots focused less on the primary flight display while spend-ing significantly more time scanning the other interfaces.The physiological fluctuating patterns associated with risky operations in SPO were highlighted by cross-correlating multimodal data.The experimental-based noteworthy insights may wish to inform commercial SPO measures to les-sen the persistent physiological fluctuation,assisting airlines in creating SPO-oriented intelligent flight systems to give captains adequate support for assuring safer air transportation.
查看更多>>摘要：A prescribed performance control scheme based on the three-inflection-point hyperbolic function and predefined time performance function is proposed to solve the trajectory tracking problem of the forward-tilting morphing aerospace vehicle with time-varying actuator faults.To accurately estimate the loss degree of actuator faults,an immersion and invariance observer based on the predefined time dynamic scale factor is designed to estimate and compensate it.A composite dynamic sliding mode surface is designed using a three-inflection-point hyperbolic function,and a novel three-inflection-point sliding mode control framework is proposed.The convergent domain of the sliding manifold is adjusted by parameters,and the system error convergence is controllable.A transfer function is designed to eliminate the sensitivity of the three-inflection-point hyperbolic slid-ing mode to the unknown initial state,and combined with the barrier Lyapunov function,and the performance constraint of the system is realized.The global asymptotic stability of the system is demonstrated using a strict mathematical proof.The effectiveness and superiority of the proposed control scheme are proven by simulation experiments.
查看更多>>摘要：Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air combat.Although Multi-Agent Reinforce-ment Learning(MARL)shows outstanding performance in cooperative decision-making,it is chal-lenging for existing MARL algorithms to quickly converge to an optimal strategy for UCAV formation in BVR air combat where confrontation is complicated and reward is extremely sparse and delayed.Aiming to solve this problem,this paper proposes an Advantage Highlight Multi-Agent Proximal Policy Optimization(AHMAPPO)algorithm.First,at every step,the AHMAPPO records the degree to which the best formation exceeds the average of formations in parallel envi-ronments and carries out additional advantage sampling according to it.Then,the sampling result is introduced into the updating process of the actor network to improve its optimization efficiency.Finally,the simulation results reveal that compared with some state-of-the-art MARL algorithms,the AHMAPPO can obtain a more excellent strategy utilizing fewer sample episodes in the UCAV formation BVR air combat simulation environment built in this paper,which can reflect the critical features of BVR air combat.The AHMAPPO can significantly increase the convergence efficiency of the strategy for UCAV formation in BVR air combat,with a maximum increase of 81.5％rel-ative to other algorithms.
查看更多>>摘要：Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose estimation.To improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on Variational Auto-Encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target spacecraft.Both methods treat the pose vector as latent variables,employing an encoder-decoder network with a Variational Auto-Encoder(VAE)structure.To enhance the precision of pose estimation,PE-VAE uses the VAE structure to intro-duce reconstruction mechanism with the whole image.Furthermore,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired shape.Comparative evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features.
查看更多>>摘要：The distributed prescribed-time orbit containment control for the satellite cluster flight with multiple dynamic leaders is investigated.The directed information communication topology between followers is taken into account in the overall paper.When the satellite mass is assumed to be constant,a distributed prescribed-time orbit containment controller is,firstly,presented to drive the followers into the dynamic convex hull produced by multiple leaders.Then,the parameter uncertainty is considered,and a prescribed-time sliding mode estimator is introduced to estimate the desired velocity of each follower.Based on the estimated state,a novel distributed adaptive prescribed-time orbit containment control scheme is proposed.The Lyapunov stability theory is uti-lized to prove the prescribed-time stability of the closed-loop system.Finally,several numerical sim-ulations and comparison of different control methods are provided to verify the effectiveness and superiority of the proposed control method.
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