To address the challenge of precise attitude stabilization control for spacecraft under varying body parameter deviations and environmental disturbances， a novel reinforcement learning-based attitude control method is proposed， which is integrated with small-world spiking neural networks （SW-SNN）. A spiking neural network with small-world topological properties is established as the core controller， and an enhanced actor-critic framework is designed， which is based on the proximal policy optimization algorithm. The parameters of the SW-SNN are updated through a spatiotemporal backpropagation algorithm that enables a synergistic mechanism for online policy optimization and offline evaluation. A Lyapunov stability reward mechanism is designed to dynamically optimize the system's energy function， which enhances asymptotic stability. Additionally， penalty terms for smoothness of attitude angle tracking error velocity and continuity are introduced to establish a joint optimization framework that integrates stability constraints with control accuracy. Simulation results demonstrate that the control system shows rapid dynamic response， high control performance and strong robustness. Under step response conditions， the settling time of control system is reduced to 0.32 seconds with a steady-state error lowered than 0.001°. Even under extreme conditions with 50% deviation in aerodynamic torque coefficients and 25% deviation in inertial parameters， the control system remains stable. the system's region of attraction is effectively expanded by applying the designed Lyapunov stability reward mechanism that ensures robust stability over a much wider operating range.

Regarding the aerodynamic-deformation-control multi-coupling problem encountered in high-speed morphing aircraft during dynamic morphing， a six-degree-of-freedom dynamic model is established and the aerodynamic moment coupling matrix is derived. The coupling strength of the roll， yaw and pitch channels is quantitatively analyzed， and the effect laws of angle of attack， Mach number， and deformation magnitude on coupling characteristics are systematically investigated. The results show that， for the given simulation case， the pitch channel is most significantly influenced by deformation， which presents a nonlinear increase followed by the growing deformation magnitude. The roll channel exhibits moderate coupling that is affected jointly by the angle of attack and Mach number， and the effect of coupling intensifies under low angle of attack and high Mach number conditions. In contrast， the yaw channel exhibits weak coupling among attitude angles， and that result shows that attitude angles coupling can be reasonably neglected in control design. Additionally， the analysis of control surface deflection coupling indicates that the roll and sideslip channels are decoupled， while the coupling degree in pitch control remains low and deformation has no significant impact on control coupling. These research results provide theoretical support for the decoupling control design and flight stability improvement of high-speed morphing aircraft.

Aiming at addressing the issue of insufficient compatibility of the dynamic detection characteristics and traditional method in the area coverage monitor task of fixed-wing UAVs， an optimal deployment strategy based on a cooperative multi-UAV joint detection-probability model is first proposed in this paper. Regarding two UAVs loitering side-by-side， the spatial detection probability and temporal coverage ratio are jointly considered， and an iterative algorithm is designed to solve and determine two UAVs optimal deployment parameters due to the purpose of maximizing the largest axis-aligned rectangle within the effective surveillance region. This approach is then extended to multi-UAV cooperative monitor missions via a square-grid layout. The simulation results show that， compared with conventional exhaustive coverage， when the spacial detection probability reaches by 90% and detection intervals shorter than 40 s， the effective monitored area is boosted by 80.8% and the number of required UAV counts by 60% reduced.

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.

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.

A design methodology for launch vehicle trajectory planning systems is proposed，which is compatible with multiple trajectory software suites and enables rapid trajectory computation with extensive peripheral support functions. The system consists of five modules about planning calculation， integrated management， comprehensive analysis， planning database and visualization. Through the systematic integration of architectural design and algorithmic optimization， the critical conflicts are solved among computational efficiency， universality， and decision-making coordination in launch vehicle trajectory planning by using proposed design methodology which provides extendable technical support for high-density launch mission and offers significant engineering applicability as well as 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.

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.

In response to the automated testing of launch vehicles and short development cycle and high quality requirements of aerospace software， an automated testing method by using configuration files is designed for test and launch control system of launch vehicles in this paper. By modularizing the functions of test and launch control software system， reusable modules are abstracted such as human-computer interaction， data communication， flow driver， data interpretation and log recording， and the flow driver module is responsible for driving oneself by working with multiple independent modules that follow its own configuration files to implement business work. During improving system automated test capability， the software re-usability is improved and the software design complexity is reduced. Thus， the system reliability is guaranteed to be improved and development progress is accelerated.

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 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 the issue of control performance degradation in vehicles under structural disturbances， aerodynamic parameter variations， and external environmental interference， a hybrid control approach that integrates the deep deterministic policy gradient （DDPG） algorithm with a traditional PID controller is proposed in this paper. The initial stable control capability is provided by PID controller， while the reinforcement learning strategy enables online adaptive tuning of the flight controller. A nonlinear dynamic simulation platform is established， which is based on the vehicle model. Experimental results demonstrate that， in typical altitude step-response control tasks， the method shortenes the system response time by 62.8%， with overshoot and steady-state error maintained within 1%. Even under complex conditions involving ±20% parameter variations， the system still retains high-precision control. Compared with the conventional PID controller， the proposed method shows superior performance in terms of response speed， stability and adaptability and can be served as reference for prospects of engineering application.

In this paper， a distributed prescribed performance cooperative guidance law is proposed for multi-vehicle with input delay. In order to improve the guidance performance， a novel continuous prescribed performance method is developed， which ensures the system error satisfies both dynamic and steady-state performance. By integrating the continuous prescribed performance method with finite-time control theory， the cooperative guidance laws based on the line-of-sight direction and the line-of-sight normal direction are designed， which realize the simultaneous attack of multi-vehicle on stationary target. The simulation results show that the multi-vehicle can hit the stationary target in performance of prescribed dynamic and steady state by applying the proposed cooperative guidance law.

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.

Aiming at solving the insufficient aerodynamic load margin， a load relief control method based on double-loop observer of force and moment is proposed for a new generation launch vehicle. The control swing angle compensation is formed through disturbing torque acquisition by usig structural observer in moment circle， which can effectively reduce attitude deviation； The attitude compensation angle is formed through disturbing force acquisition by using structural observer in force circle，which can effectively reduce velocity deviation and attack angle. The load relief effects of traditional accelerometer feed-back control in steady wind is improved by using this method that keeps load relief ability in shear wind and meanwhile promotes nominal trajectory tracking effect. The simulation results show that attitude deviation and velocity deviation is significantly decreased and load relief effect can reach by 20%， that can effectively increase the launch probability.

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.

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.

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.

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.

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.

In order to avoid the extra computational burden inherent in traditional modelling methods， the six-degree-of-freedom twistor is employed for the pose integrated description of a quadrotor UAV that realizes compact pose parameterization and establishes a singularity-free and non-redundant dynamic model. Based on Lyapunov stability theory， a novel controller integrated a hyperbolic tangent sliding surface with a hyperbolic cosine switching term is designed， and rigorous proof of the closed-loop system's global asymptotic stability is provided via LaSalle's invariance principle. Finally， validation is conducted by using the Links-RT hardware-in-the-loop simulation platform. The results demonstrate enhanced dynamic performance， superior disturbance rejection and improved real-time capability compared with conventional schemes， and confirm the method's feasibility in engineering.

The consensus problem of general linear multi-vehicle systems with communication delays is investigated. Firstly， a model predictive control （MPC） method is proposed to compensate for communication delays occurring among controllers and actuators， controllers and sensors， as well as neighboring vehicles. Secondly， in order to conserve resources and reduce communication frequency and control update frequency among vehicles， a dynamic event-triggered model predictive control method is developed by integrating MPC with event-triggering mechanisms through differential equation-based dynamic term configurations. By using this proposed control algorithm， consensus achievement is confirmed under communication delays and system stability is guaranteed through theoretical analysis. Finally， the results of numerical simulation experiments verify the effectiveness of the proposed algorithm. Compared with periodic triggering， this method reduces triggering frequency by 76.4% and achieves lower triggering frequency by 31.4% than static event-triggered approaches， and communication and computational resources of the system are effectively saved.

To address the issue of pose control challenges of de-icing UAV systems under wind field disturbances and de-icing collisions， an integrated particle swarm optimization sliding mode control method is proposed， which is based on twistor theory. A dynamic model of de-icing UAVs incorporating both wind disturbances and collision impacts is established in this approach， which achieves realization of unified description of positional and attitudinal motions through twistor theory. On this basis， an adaptive gain adjustment mechanism is employed to coordinate pose control， which realizes integrated motion control under framework of the twistor theory.The results of simulation experiments ultimately demonstrate that the trajectory tracking accuracy and disturbance rejection capability are significantly enhanced by using this proposed method under complex working conditions involving coupled wind disturbances and collision effects.

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.

Regarding the multi-spacecraft formation flying， a distributed formation tracking control scheme that integrates active disturbance rejection control with prescribed performance control is proposed， considering model uncertainties and external disturbances. Firstly， based on the principle of active disturbance rejection control， an extended state observer is employed to estimate and compensate for system uncertainties and external disturbances. Secondly， a prescribed performance-based controller is designed to enable the multi-spacecraft system to form and maintain the desired formation configuration， while ensuring fixed-time convergence. On this basis， the stability of the closed-loop controlled system is proved by using Lyapunov theory. Comparative simulations are conducted to validate the superority of the proposed designed on transient and steady-state performance.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.