The hyperbolic orbit plane of the planetary gravity assist is determined by the entry speed and escape speed and based on two-body dynamics. The engagement and escape hyperbolic orbit's asymptotic angle are adjusted by the height at the near-planet point. Combined with the accelerating/braking speed increment near the planet， the direction of the escape velocity is determined. Based on the conversion algorithm between the heliocentric elliptical orbit and the planet's hyperbolic orbit， as well as the iterative algorithm for the height near the planet， the transfer orbit is quickly calculated， and the parameters of the approach/escape hyperbolic orbit are determined. By taking the 2015 XF261 asteroid defense as an instance， a Venus-assisted transfer orbit design is implemented， and the transfer orbit， the transfer duration and velocity increment requirements of the probe are presented. The simulation results show that rapid calculation of the analytical solution of the planetary gravity-assisted transfer orbits can significantly reduce the search time for transfer orbit.

An intelligent forecasting model based on ensemble learning， named iTransformer-XGBoost， is proposed in this paper to address the issue of precision limitations of traditional time series prediction models. Firstly， the Pearson correlation coefficient is employed to select the key features affecting time series data and construct an optimized input dataset. Then， the iTransformer model is utilized to capture long-term dependencies within the time series and generate preliminary prediction results. Meanwhile， the XGBoost algorithm is introduced to achieve nonlinear modeling of the time series data. Finally， a threshold-based combination strategy is applied to the prediction results fusion of iTransformer and XGBoost， thereby determining the integrated output and improving overall forecasting performance. The model is validated by using photovoltaic power-related data， and the experimental results demonstrate that the proposed approach achieves higher accuracy and stability in time series forecasting compared with traditional methods. Furthermore， it shows great potential for application in aerospace photovoltaic scenarios.

To address the issue of low test coverage caused by implicit test scenarios such as missing boundary conditions and exception handling in software requirement documents， a code-enhanced requirement analysis method is proposed. The code-requirement associations are established through the proposed method which is based on semantic vector similarity and LLM verification， and five types of implicit test scenarios （boundary conditions， error handling， resource management， state transitions and performance stress） are extracted from function call chains to enhance requirement descriptions， and the enhanced requirements are decomposed into test function points and scenario-driven test cases are generated. The results of experiments on open-source projects show that compared with the baseline method by using LLM directly， the significant improvements are achieved by using CERA method in comprehensive test quality and test requirement coverage， which maintains higher API test accuracy. The effectiveness of three core components： scenario extraction， two-stage matching strategy and BERT-based rough screening is verified through ablation experiments. The good adaptability on both parsing libraries and embedded systems is demonstrated by the results of proposed method applied that is particularly suitable for third-party testing and acceptance testing scenarios.

The selection of stable spin angular velocity for aircraft is focused in this paper. A method for choosing a stable angular velocity based on the spin stability criterion of aircraft is researched and proposed. Through theoretical analysis of the coning motion dynamics of spinning vehicles， the coupled effects of static stability moment， Magnus moment， and damping moment on the directional stability of the projectile body are fundamentally revealed. Based on a coning motion angular dynamics model under the constraints of deviations between the center of mass and the center of pressure， a system characteristic equation is established and the Routh-Hurwitz stability criterion is then employed to formulate a dynamic stability criterion. Furthermore， on the basis of the traditional three-degree-of-freedom trajectory model， the derived dynamic stability criterion for coning motion is used to deduce the feasible region for the spin angular velocity. A stable spin rate scheme for the aircraft is subsequently designed based on this region. Numerical simulation results demonstrate that the coning motion divergence is effectively suppressed by using proposed method that simultaneously reduces control energy consumption in the pitch/yaw channels. The achievement presented hereinabove provide a technical pathway to directional stable flight with low-energy-consumption attitude control by applying the spin characteristics of aircraft， which can offer significant theoretical and practical value.

Regarding circular restricted three-body subject， a prediction method based on deep neural network is proposed for two impulse Earth-Moon transfer orbit. Firstly， three types of two-impulse transfer orbit family are selected， and the state quantities of each transfer orbit are used to establish two-impulse transfer orbit data set in which input state and predicted state of orbit are determined. Secondly， a deep neural network is constructed for orbit state prediction， and， each class of two-impulse transfer orbits in the data set is used as the training set， and the neural network is trained by the track data in the training set. Finally， according to the training results， the state quantities about transfer orbit are predicted by setting the initial state of the low-Earth orbit， and then the optimal Earth-Moon two-impulse transfer orbit in each orbit family is selected according to the prediction results. The simulation results show that the application of deep neural network can quickly predict initial state of orbit， and the prediction results have fairly smaller error intervals and can be applied to selection of optimal two-impulse transfer orbit.

To address the issue of development requirement of trusted embedded systems， a trusted embedded hardware platform is designed， which is comprised of an full indigenous Root of Trust （RoT）， Loongson processors， Kunlun firmware and SylixOS domestic technology stack. On this basis， firstly， guided by an active defense philosophy， a dynamic runtime trusted verification mechanism is researched and designed for software code. Then， this mechanism performs real-time verification of software identity and code integrity both at program startup and execution， thereby preventing the execution of unauthorized programs or tampered code. Finally， a prototype of the trusted embedded platform is established and the proposed designs are validated. The experimental results demonstrate that the trustworthiness of equipment software can be effectively enhanced by using the proposed methodology.

To address issue of the inefficiency in collaborative design of launch vehicles flight sequences， a model-based systems engineering （MBSE）based methodology is proposed for modeling launch vehicles flight sequence and the collaborative design mechanism is investigated for such flight sequence within an MBSE framework. By focusing on a representative two-stage configuration and non-booster based launch vehicles， the organizational relationships are analyzed among distinct flight phases throughout the vehicle's life-cycle and a hierarchical architecture global scheme is presented for flight sequence modeling. For each hierarchy level， based on the structural composition of the sequence command skeleton， a meta-modeling approach is developed to establish elementary models of flight sequence command signals. On the basis of these elementary models， a comprehensive MBSE model based on the entire procedure of launch vehicles flight sequence is designed. Furthermore， oriented to the collaborative design strategy for flight sequence in the MBSE framework， the model distribution and merging mechanisms based cooperative design of flight time sequence is implemented. This work lays a foundation for the full-scale digital transformation of launch vehicles design processes.

A meta-reinforcement learning method based on the universal policy-online system identifier structure is proposed to address the issue of terminal guidance with large environmental uncertainty and diverse task types. The method consists of a reinforcement learning training phase for the UP and a supervised learning phase for the OSI， and ensures reliable convergence of the training in multi-task environments through a phased transfer learning design and a pseudo-Monte Carlo-based small-variance policy gradient estimation， so that the resulting guidance policy can be adapted to varieties of terminal guidance task scenario. Simulation results show that the resulting guidance policy can meet the requirements of terminal position and path angle error in multiple terminal guidance scenarios.

To address the issue of guidance information extraction for strapdown phased array seekers with radome errors， a guidance information extraction method based on Kalman filtering is investigated. Firstly， a relative motion model is established in the body line-of-sight coordinate system， incorporating the radome error slope into the model， regarding the maneuvering targets， then the Singer maneuver model is employed， the radome error slope measured offline is treated as a known quantity， and observability analysis is implemented for the system of angle measurement observations undertaken， providing a theoretical basis for guidance information extraction in the presence of radome errors. Next， according to the isolation rising caused by gyroscope and beam control delays， a gyroscope delay compensation method is adopted to reduce the impact of airframe attitude disturbances. Finally， comparative simulations on isolation with and without gyroscope delay compensation for gyroscope and beam control delays are performed along with simulation validation in a high-altitude interception scenario against maneuvering targets. The simulation results demonstrate that the missile's isolation performance is improved after radome error compensation and a smaller miss distance is achieved in high-altitude interception of maneuvering targets.

To address the issue of requirements for efficient processing and precise decision-making of multi-source heterogeneous fault data during implementing modern aerospace missions， a large-language-model-based aerospace equipment fault scheme is proposed， which follows agent architecture to build a four-layer closed-loop system of "data-knowledge-decision-application" that integrates core functions such as fault process mining， historical case matching， process node response and plan report generation. In terms of technical realization， a two-stage mining algorithm based on process intermediate representation is used to extract the structured disposal process， historical cases are associated through combining BM25 with semantic vector hybrid matching algorithms， and knowledge graph embedding technology is relied to achieve semantic alignment of faults and process nodes， and the automation driven fault disposal plans is ultimately generated. This architecture is verified in a leakage current scenario within the control system of a launch vehicle. The intelligence and standardization level of fault handling is significantly improved and presented technical support can serve for rapid response to aerospace equipment fault plans.

Regarding the limitation of existing feedback gain designs based on specific nominal trajectories， a tracking guidance method for Mars powered descent in wide area based on control contraction metric （CCM） is proposed to match the emerging onboard trajectory planning capability. The CCM conditions for the powered descent model are analyzed. The CCM matrix and system dynamics are parameterized and approximated as polynomial functions， and then the CCM matrix is solved offline by using the method of sum of squares programming. During the flight， the control input is obtained through numerical integration based on the CCM matrix. The simulation results show that the nominal trajectories of different initial and final motion states and flight durations can be tracked by using the contraction control method under the conditions of specified range of mass and thrust.

In order to achieve the synergy between periodic self-operation control and task-driven non-periodic random control， an intelligent attitude control method is proposed for launch vehicles， which is based on reinforcement learning model predictive control （RMPC）. Firstly， a break point identification method based on time-frequency analysis is established to address the issue of switching requirements between periodic orbit maintenance and non-periodic maneuver adjustment during rocket flight. By monitoring the spectral characteristics of control command sequences in real-time， the transition boundary between periodic and non-periodic time processes is accurately located， which can serve as decision-making basis for control mode switching. Secondly， a RMPC framework integrating adaptive attention mechanism is proposed， which can dynamically adjust the prediction model structure and iterative optimization steps based on fuzzy neural network according to the type of control task， while ensuring prediction accuracy and controlling model complexity. Finally， an environment aware time-domain decision-making method is designed to enhance the adaptability of RMPC in complex environments by adaptively adjusting the predicted and control time domain lengths through online evaluation of external interference strength and system state uncertainty. The method is applied to closed-loop control simulation of typical flight scenarios of launch vehicles， and the experimental results show that trajectory tracking control precision can be maintained by using the proposed method in both periodic orbit maintenance phase and non-periodic maneuver phase.

A majorant-based control method is proposed for quadrotor unmanned aerial vehicle trajectory tracking to enhance system robustness and tracking precision. Firstly， an extended state observer is designed for the position loop to compensate for the effects of external disturbances. Secondly， a position loop controller is developed via majorant systems. Furthermore， regarding the inherent cascade-control structure of quadrotor system， the design methodologies for both the observer and controller are concurrently applied to the attitude loop. Simulation results demonstrate that the proposed control method achieves convergence of the steady-state error to specified values， enables high-precision trajectory tracking and presents computational efficiency of practical implementation.

New-type missiles designed for attacking maritime surface targets are key strike forces in modern naval warfare， which impacts on the balance of maritime power through the evolution of their flight speed， guidance methods and penetration capabilities has exerted a profound influence on the balance of maritime power. A systematic review on the technological progression of such missiles development from subsonic and supersonic systems to emerging hypersonic grade， and key technologies are examined from multiple perspectives， including propulsion systems， blackout-mitigation techniques and advanced flight control methods. Furthermore， the development trends of multi-source information fusion， intelligent mission assessment and cooperative strike operations are focused， which are expected to shape the future trajectory of maritime surface-attack missile systems.

In response to the subjects of long development cycles， requirement verification difficulty， interfaces unclearness and processes amendment complexity in traditional system engineering development， the thrust regulation controller is taken as an example in this paper to conduct research on model-based design methods. MagicDraw known as tool is used for system modeling and behavior simulation， SCADE is used for the tool of software design and code automatic generation， and ModelCenter is used for joint simulation of system behavior model and software design model. It is an exploration and research on the specific implementation of model-based systems engineering in complex aerospace systems engineering. This method can serve as important application value for the development of complex control systems and the new ideas and technical paths are suggested to design the future spacecraft thrust adjustment control.

The issue of high-reliability numerical computation software is addressed in aerospace embedded systems being susceptible to floating-point bugs， and a static symbolic execution-based method is proposed for detecting floating-point bugs in numerical software. Through the method procedure， the floating-point expression is established by symbolization under related constraints， and interval computation and interval constraint propagation is introduced in non-relational numerical abstract domains， and math functions are symbolically modeled in order to improve the accuracy of detecting floating-point bugs. Regarding application to actual floating-point numerical software and aerospace testing， accuracy and efficiency are effectually improved， and multiple genuine floating-point bugs are successfully identified and confirmed by developers， which significantly enhances the reliability of aerospace software.

Aiming at the problems of over-segmentation and text adhesion in natural scene text detection algorithms based on image segmentation， a natural scene text detection algorithm based on a cross-level attention mechanism is proposed. By designing a cross-level attention module， the network's focus on key features and contextual information in high-resolution feature maps is enhanced by applying the proposed algorithm that thereby improves the integration capability for fragmented text. Through the design of a feature decomposition and reorganization module， the fused features are decomposed into high-frequency and low-frequency components that enhance the network's ability of distinguishing text boundary regions. After integrating these two modules into the baseline model， performance tests are implemented on two mainstream datasets. The experimental results show those of current mainstream algorithms are all surpassed， and compared to the baseline model， the missed detection rate and false alarm rate are both declined.

A disturbance observer-based adaptive fuzzy model predictive control method is proposed to solve the attitude control difficulty of quadrotor UAVs in complex environments. A nonlinear dynamic model of the quadrotor UAV is established， including external disturbances. In order to effectively handle system nonlinearities and constraints， the T-S fuzzy modeling approach is employed to transform the nonlinear model into a set of local linear models which are then embedded into a model predictive control framework for rolling optimization solution. Meanwhile， an adaptive prediction horizon adjustment strategy is designed to dynamically optimize controller parameters， which is based on the system's dynamic characteristics to optimize controller parameters in real time and effectively balances dynamic response speed and robustness. Furthermore， by establishing a Lyapunov function， stability analysis of the state equations is conducted， rigorously proving the asymptotic stability of the system under this adaptive strategy. The simulation results demonstrate that outstanding stability control capability and strong robustness performance is presented by applying proposed method in complex uncertain environments.

In response to the demand of intelligence and integration in ground-based testing and launching control systems for launch vehicles， an intelligent and integrated architecture is proposed， which is comprised of test， launch and control functionality， auxiliary decision-making functionality and integrated storage-access-computation functionality. Regarding the test and launch control functionality， a systematic approach by featuring comprehensivization， modularization， and integratization is proposed， and the designs of module functions， system functions and the software architecture are completed. Regarding auxiliary decision-making functionality， a multi-agent collaborative auxiliary decision-making system based on a knowledge base of historical faults and contingency plans is developed， which achieves the objectives of rapid response and precise decision-making. Regarding the integrated storage-access-computation functionality， a lightweight intelligent computing platform based on large language models is proposed， which is realizes the integration and intelligence of data storage， data analysis and related capabilities.

Addressing the issues of challenge for balancing fuel economy， bus voltage stability and battery lifespan in energy management strategies for turbo-electric hybrid vertical takeoff and landing （VTOL） UAVs， a component-level simulation model of the hybrid power system is established， which incorporates a turbine generator system， battery energy storage system， three-level rectifier and bidirectional DC/DC converter， and a kind of two-layer model predictive control （MPC） energy management strategy is proposed. The upper-layer MPC is targeted for optimization by minimized fuel consumption， and generator output constraints， state-of-charge （SOC） variations and battery degradation factors are combined in order to achieve multi-timescale power forecasting and dynamic allocation. The lower-layer MPC focuses on DC bus voltage stability， employing duty cycle control of the bidirectional DC/DC converter for rapid voltage regulation. Simulation verification is implemented under typical long-endurance and pulsed-load composite operating mode in offshore flight environment， which demonstrates the strategy's significant advantages of enhancing energy efficiency， reducing battery life degradation and improving system stability.

The sneak circuits in two grounding designs for launch vehicle electrical systems： a grounding shell and a floating ground-grounding shell are analyzed， and the sneak circuits are presented in both designs. Based on electromagnetic field theory， a sneak circuit model is established for the launch vehicle electrical system grounding system under transient high voltage and electromagnetic radiation conditions. The impact of transient high voltage and electromagnetic radiation on the operation and failure phenomena of the launch vehicle electrical system is analyzed theoretically. Based on the sneak circuit model， grounding design principles are proposed for both grounding schemes， which suppress sneak circuits in the electrical system. The analytical approach and methods presented in this paper can serve as a reference for other designers and the reliability and robustness of launch vehicle electrical systems can further be improved.

An universal program parsing and test coverage analysis technology is introduced for multi-architecture embedded processors， including program parsing for compilers of different architectures and universal data storage structure， a kind of fast program information retrieval algorithm and a universal test coverage analysis architecture. By taking multiple embedded software onboard system of the new-generation launch vehicle as validation targets， the verification of each technical aspect is implemented. The validation results show that the proposed method is compatible with multiple compiler standards and processor architectures， and the provided universal coverage test framework can meet the requirements of mainstream embedded processors. The proposed method is extensible and can be extended to applications to the software that has new target processor architectures in the future.

In response to the impact of spaceborne radars installation error on data-fusion accuracy， a multi-radar spatial registration algorithm based on same target observations is proposed. The measurement vectors of the same target obtained by different tracking radars is processed by using this algorithm under a common reference coordinate system through cross-product operations， and the cross-product vectors are projected onto a specified plane to establish the relationship with the relative installation bias angles， and the unbiasedness of the least squares estimation algorithm is verified by analyzing the characteristics of derived observation errors. Through actual on-orbit satellite conditions based simulation experiments， the results demonstrate that the proposed method can reliably and rapidly achieve spatial registration of different radars without relying on satellite attitude measurement.

An aerospace evolutionary control system is proposed. Firstly， combining typical scenarios and the challenges faced， and the theoretical basis and research significance of evolutionary control system are specified. Then， by starting from basic concepts， a comprehensive definition of evolutionary control systems is established， and their key characteristics are deeply analyzed. Moreover， in order to realize implementation， discussions are conducted from the two dimensions known as design objectives and development planning. In addition， the specific schemes for transferring evolutionary control systems into practical applications are proposed. Finally， comprehensive explorations and practice are conducted from the two perspectives of technology development and equipment application towards this system.

In order to solve the UAV path planning problem under complex threat scenarios， an improved hybrid intelligent optimization algorithm termed HGWOSCA is presented， which synthesizes the merits of the grey wolf optimizer （GWO） and the Sine Cosine algorithm （SCA）. The algorithm's performance of globe search ability and partial development precision is significantly improved through the integration of a Circle chaotic initialization strategy， a nonlinear oscillation control parameter a， and an piecewise Sine selection strategy. On this basis， a mathematical model adaptive to UAV trajectory planning is developed， and cubic spline interpolation is applied to smooth the generated trajectories， thereby their feasibility and adaptability are enhanced. Simulations are conducted in comprehensive scenarios under varied threat conditions to validate the algorithm's performance. Experimental results confirm that HGWOSCA consistently has better performance than seven benchmark metaheuristic algorithms through all test scenarios， which shows fairly good robustness and superiority.

A specification and formal verification method is presented for dynamic failure modes in aerospace launch systems. The dynamic fault model designed through this approach enables real-time fault diagnosis systems in the aerospace field to possess diagnostic capabilities for complex and diverse fault characteristics. The dynamic fault models are represented by temporal facts， however， the complexity and abstract nature of temporal facts make them difficult to be validated through manual analysis or testing. By describing the semantics of temporal symptoms through temporal logic formulas， the correctness of the fault model is allowed to be verified through model checking methods. This verification process can be automated by using model checking tools. Moreover， the dynamic fault model does not require formal specifications， which allows domain experts to focus solely on domain-specific issues when constructing fault models. In aerospace engineering practice， the key properties of fault models through automated verification of model checking methods can provide domain experts with a reliable and verifiable mathematical approach for designing fault models.

The precision contribution of the missile-borne hybrid inertial navigation system（HINS） in missile-borne platform application is researched in this paper. On the basis of clarifying the principle and characteristics of HINS， its theoretical advantages in the mission chain of missile usage are analyzed. The error models of HINS is provided and its propagation characteristics are both analyzed. The impact on the precision contribution of HINS is focused towards researches regarding the errors of inertial instrument， initial alignment errors， trajectory characteristics， rotation modulation schemes， the integrated navigation scheme and the other factors. The reasons for the different opinions on the precision advantages of HINS in the related professional fields are analyzed， and a much more reasonable evaluation scheme for the precision advantages of missile-borne HINS is further proposed to support and improve the precision evaluation system of missile-borne HINS， which can serve as important reference for the engineering application of missile-borne HINS.

In order to standardize usage of static test tools， combining the automation with platform-based characteristics of tools， a static testing multi-tool collaborative analysis framework supporting distributed deployment and parallel analysis is designed； A remote driving technology based on a distributed architecture is proposed， which establishes a test task queue mechanism and tool scheduling mechanism and thereby enables programmatic control of remotely driven tool analysis； A multi-source information fusion technique is developed to achieve normalization， which realize filtering and integration of analysis results from multiple tools. By applying these technologies to static testing across various aerospace domains， testing efficiency and quality are effectively improved.

To address the issues of local minima and parameter sensitivity in the traditional artificial potential field （APF）， as well as the slow convergence and low sample efficiency of deep reinforcement learning in UAV path planning in complex unknown environments， an adaptive path planning algorithm fusing proximal policy optimization （PPO） and improved artificial potential field （PPO-APF） is proposed. A hierarchical architecture is applied in this algorithm. The repulsive force coefficient， effective distance， and rate adjustment coefficient are adaptively outputted by the upper-layer PPO network based on local perception， and flight commands are generated by the lower-layer improved APF model， where a target distance factor and kinematic constraints are introduced. Meantime， a composite reward function incorporating path smoothness with parameter stability is designed to suppress path jitter. Simulation results demonstrate that in complex scenarios with 22% obstacle coverage， the planning success rate of PPO-APF reaches 97.33%， which is significantly superior to the improved APF by 53.67% and pure reinforcement learning based PPO by 85%. The proposed algorithm takes advantage of a significant "cold start" mode， which effectively combines the adaptability of DRL with the stability of APF to realize UAV's autonomous learning of slow-down crossing on narrow passage and preemptive avoidance strategies， etc.

To addresses the attitude tracking control problem of reusable launch vehicle under the influence of model uncertainties， an adaptive fixed-time reinforcement learning-based optimal backstepping control strategy is proposed in this paper. Firstly， a fixed-time controller based on the hyperbolic tangent function is designed to avoid the singularity issue in traditional fractional-order fixed-time controllers. Secondly， regarding handling system model uncertainties， a radial basis function neural network is employed for approximation， and a series-parallel estimation model is introduced to enhance estimation accuracy and convergence speed. On this basis， an "Identifier-Actor-Critic" framework is established， and an improved weight adaptive update law is designed to solve the Hamilton-Jacobi-Bellman equation， and a trade-off is achieved between tracking performance and control input. Finally， the fixed-time stability of this proposed method based on Lyapunov theory is proven， and numerical simulations are conducted to verify the effectiveness of the algorithm.

Regarding a guidance scenario of the multiple projectiles that simultaneously attack a stationary target at a preset time in three-dimensional space， a cooperative guidance law satisfying fixed time convergence is proposed. Firstly， a three-dimensional guidance constant velocity model for missiles and guidance error variables are established. Next， an input variable consisting of a fixed time consistency controller and an error convergence controller is designed to establish a first-order multi-agent system that satisfies time constraints. Then， the collaborative guidance law of proportional guidance term and time synergy term is derived from the system state equation. Finally， the effectiveness of the proposed derivative law is verified through numerical simulation.

According to tight coupling of DDS and flight control software in the scenarios of aerospace software， difficulty in perceiving the application layer state， and inflexibility of static QoS policy for data distribution service， the TSM-DDS framework is proposed， which is a kind of DDS-based communication service architecture for aerospace equipment applications. This architecture guarantees the high reliability of aerospace communication through a triple mechanism： 1） a dual-task inter-process communication architecture that enables the asynchronous decoupling of the control and communication domains； 2） a transaction management-based handshake and retransmission mechanism that achieves perceiving execution state at the application layer； 3） a dynamic traffic control policy that ensures the high reliability of control information in high-concurrency transmission scenarios. The simulation results demonstrate that the TSM-layer handshake and retransmission mechanism can accurately perceive the execution state of the peer application layer and effectively guarantees the execution reliability of control messages. Furthermore， in high-concurrency packet loss-prone environments， the transmission reliability of control messages can be effectively safeguarded by deploying the dynamic traffic control policy. Ultimately， the complex QoS configurations of DDS is simplified by the proposed framework that provides a reusable technical paradigm for the engineering implementation of DDS in the aerospace control domain.

With the continuous applications expansion in the aerospace domain， the health management of complex equipment is oriented from scheduled maintenance towards data-driven predictive maintenance. A data-driven anomaly detection framework known as TS-ADF is proposed， which achieves effective identification of potential anomalies through the establishment of normal patterns， reconstructive analysis and feature fusion of multidimensional operational data. Specifically， preliminary screening is involved by using density peak clustering， deep features of time series are captured through LSTM-AE， and anomaly points are validated via time-frequency analysis and parameter variation analysis. Experimental results demonstrate the method's effectiveness in anomaly detection， which can serve intelligent health management and predictive maintenance of equipment.

Aiming at the problems of resource heterogeneity， complex constraints and difficult dynamic scheduling in hybrid-architecture measurement and control systems， a dynamic resource scheduling method named MA-DRS based on multi-agent deep reinforcement learning is proposed. The multi-objective， multi-constraint scheduling problem is modeled as a Markov decision process， and a multi-agent collaborative framework based on centralized training and decentralized execution is designed. The key techniques such as heterogeneous feature embedding， prioritized experience replay and dynamic action masking are introduced to improve learning efficiency and decision-making rationality. Simulation experiment results show that， compared with fixed combination， rule-based scheduling， genetic algorithm and single-agent DQN， resource utilization rate， task completion rate， and response speed are significantly imporved by using this proposed MA-DRS method in the scenarios of normal， high-concurrency and fault-injection which demonstrates excellent adaptability and robustness. It can serve as an effective solution for intelligent scheduling in complex measurement and control systems.

Regarding the investigation of a cluster of flight vehicles by using multi-stage solid rocket boosters， the research is implemented for the collaborative trajectory optimization. In order to facilitate the collaborative task during the re-entry phase and ensure the optimum energy performance of the cluster by eliminating individual state errors， a multi-layer collaborative trajectory optimization algorithm is proposed， which is based on the hp adaptive pseudo-spectral method. By designing different planning models in the top， middle and bottom layers， the algorithm achieves time-coordinated reentry. Firstly， a trajectory planning model is established， which is based on the flight characteristics of the booster phase， and sparse collocation points are used to determine the cluster's temporally coordinated interval. Then， the single-vehicle planning model is extended in dimension， and after introducing constraints on the rate of change of control variables， the hp adaptive pseudo-spectral method is applied to quick resolving for the desired terminal state. Finally， additional collocation points are incorporated for refined optimization. Simulation results demonstrate the effectiveness of the proposed method by enabling rapid and effective optimization of the flight vehicle cluster， which meets the requirements for collaborative trajectory planning.

Aiming at the attitude control of reusable launch vehicle in reentry phase under restrains of parametric uncertainties， external strong disturbances and compound actuator faults， an adaptive sliding mode fault-tolerant control method based on composite disturbance observer （CDO） is proposed. Firstly， the RLV attitude tracking error model considering actuator effectiveness loss and bias faults is established. Then， a cascade and fused CDO is designed to accurately estimate the lumped disturbance. On this basis， a chattering-free adaptive sliding mode control law based on the disturbance compensation strategy is designed， which employs the online adaptive gain to replace the discontinuous switching subject and ensure fixed-time convergence while eliminating chattering. The simulation results show that compared with existing methods， the tracking accuracy， convergence speed and control smoothness are significantly improved by using the proposed scheme which is under fault scenarios with extreme disturbances and verifies its superior fault-tolerant performance and robustness.

Excessive axle temperature of vehicle can lead to a series of issues that affect driving safety and is an important index for evaluating the environmental adaptability of special vehicles. An equipment axle temperature prediction method based on a thermal equilibrium model is proposed. A differential equation describing the thermal variation of vehicle axles is firstly formulated. Parameters of the equation are calibrated by utilizing experimentally acquired data， and the equation structure is further optimized. Consequently， axle temperature predictions with a margin of error below 3°C are achieved， thereby the challenging problem is to forecast axle temperature fluctuations in special vehicles， which is solved under diverse environmental conditions.

Software factories can serve as key platforms for the efficient development of aerospace software. The dynamic interaction and real-time update of their frontend components have direct influence on development efficiency and resource utilization. Traditional fixed-time-interval refresh mechanisms are liable to cause resource waste or data latency issues， while existing semi-automatic adjustment methods struggle to adapt to complex scenarios in software factories， such as multiple component types， cross-network deployment and multi-environment deployment. An adaptive refresh algorithm is proposed for frontend components. The change values can be obtained by component differential comparison， and a CVI （Component volatility index）is established， which integrates short-term change trends with long-term average levels to quantify the volatility of business data， and scenario-specific adjustment strategies are designed. This algorithm can adapt to scenarios like multi-team collaboration and high-frequency component reuse， which effectively balances resource consumption and response efficiency and thus provides support for the efficient operation of software factories.

In response to the environmental adaptability analysis challenges which arise due to flexible electronic technologies applied to aerospace， a study is conducted on simulation methods for space environment adaptability of hybrid integrated circuits based on flexible chips. Firstly， combined with the characteristics of flexible hybrid circuits， weak objects in the environments of bending stress， thermology and mechanics are targeted. A multi-dimensional selection of flexible chips， packaged devices and flexible circuit boards is made for modeling and simulation method research. Subsequently， this method is applied to simulation analysis on a kind of flexible hybrid circuit， and the simulation results are used to guide the design and optimization of the circuit. The research results indicate that this method has guiding significance for initially assessing the performance of flexible integrated circuits in complex space environments， which lays a foundation for promoting the application of flexible electronic technology in aerospace industry.

A robust adaptive collaborative positioning algorithm is proposed to address the issue of ultra-wideband （UWB） ranging that is susceptible to interference from non-line-of-sight and multi-path effects on unmanned vehicle cluster collaborative positioning within global navigation satellite system-denied environments. Firstly， a robust random cut forest method is applied to perform anomaly detection by using the ranging innovation and the UWB first-path power ratio as input features， and a Huber weighting matrix based on UWB signal strength is established to correct abnormal ranging measurements. In addition， a variational Bayesian adaptive mechanism is introduced to estimate the measurement noise covariance online， which results in the formation of proposed RRCF-Huber-VBSRCKF algorithm. Experimental results show that， compared with the standard SRCKF， the average localization error is reduced from 0.23 m to 0.14 m and improved by 39.1%； compared with the RRCF-Huber-SRCKF， the average localization error is reduced from 0.18 m to 0.14 m and improved by 22.2%， and the maximum error is reduced from 0.5 m to 0.39 m， which demonstrate the superiority of the proposed algorithm.