Under the background of the vigorous development of theory and application of embodied intelligence， in order to further improve the intelligence level of low altitude UAVs and expand their application boundaries， the development requirements of low altitude UAVs are focused on the context of low altitude economy， and the researches are implemented and systematically analyzed， regarding three domains in terms of the theoretical basis， key technology paths and application challenges. Firstly， the advantages and research value of embodied navigation compared with traditional navigation on semantic understanding， environment interaction and group collaboration are determined. Secondly， derived from the development of traditional simultaneous location and mapping（SLAM） to the technology evolution of visual language navigation（VLN） and visual language action（VLA） model， one special navigation technology framework for low altitude UAVs is established. Thirdly， through analysis of two typical application cases， the practical application trend of low altitude UAV in complex environment is discussed. Finally， future development directions of low altitude UAV embodied navigation are overviewed， the proposed achievement can serve as valuable references for theoretical research and industrial application to intelligent autonomous navigation.

To address the challenges of insufficient control stability， limited navigation precision and poor generalization ability encountered by unmanned aerial vehicles during implementation of autonomous visual navigation tasks， a brain-inspired convolutional neural network-spiking recurrent neural network （CNN-SRNN） is proposed to achieve robust end-to-end stable flight navigation strategies with strong generalization capabilities. This network architecture simulates the flight control circuit of the fruit fly brain， which uses CNN network by extracting visual features to form high-level state representations and integrates with an attention mechanism for precise target recognition and localization. A spiking recurrent neural network （SRNN） serves as the flight navigation controller， which realizes time sequence motion information integration and flight control. Additionally， a regularization strategy based on Gershgorin disc theory is designed to enhance the stability of navigation control. Evaluations of UAV navigation performance through diverse simulation environments demonstrate that CNN-SRNN network has the outstanding scene-generalization capability， robustness against noise and decision-making stability. The encoding and decoding relationship between neural activation patterns in SRNN and UAV flight trajectories is further analyzed， and the navigation control mechanisms of brain-inspired neural network are revealed and model interpretability is significantly improved.

Traditional scene matching methods for unmanned aerial vehicles （UAVs） in low-altitude environments often suffer from ineffective outlier rejection， leading to degraded positioning precision. To address this issue， an improved scene matching localization algorithm is proposed in this paper. Firstly， initial data are generated by using triple relationships in this algorithm. Subsequently， a ternary matching optimization method is introduced by combining triangular feature similarity measurement and maximum Euclidean distance screening to reduce computational costs and enhance matching correctness. Furthermore， a data refinement strategy is adopted to improve the sampling performance of the algorithm. Simulation results demonstrate that the proposed algorithm achieves superior accuracy and real-time performance for UAV scene matching localization in complex low-altitude environments， which significantly improves computational efficiency and positioning precision.

To address the issues of complex modeling procedures of conventional methods for quadrotor UAVs carrying time-varying slung loads and the poor adaptive capability of traditional PID controllers under complex wind disturbances， a Kane's method-based dynamic modeling approach and a model reference adaptive control （MRAC） method with adaptive learning rates are proposed. Kane's method takes advantage of combination of forces and partial velocities， which eliminates the need for explicit analysis of cable constraint forces required by Newton-Euler formulations and bypassing the Lagrangian function established with second-order derivative computations， thereby the calculation process is simplified. The adaptive learning rate's MRAC method enables quadrotor UAVs to resist composite disturbances from wind and time-varying load variations through adaptive learning rates application and variable parameter control， which achieves precise position and attitude control. Simulation results show that under composite disturbances from time-varying loads and complex wind fields， the adaptive learning rate's MRAC demonstrates superior performance in both overshoot suppression and convergence rate compared with conventional MRAC.

To address the issue of safety limitations of traditional path planning algorithms in dense obstacle environments， a Voronoi diagram-based safe obstacle avoidance algorithm is proposed for polygonal obstacle regions. Firstly， a circular coverage model with minimal overflow rate is established to optimally encapsulate obstacle areas. Subsequently， a Voronoi diagram construction algorithm is designed， which is based on the circular coverage to generate a navigable skeleton of the free space. Furthermore， a path generation method integrating unmanned aerial vehicle （UAV） kinematic constraints is developed by using the skeleton. Finally， cubic B-spline interpolation is applied for ensuring path smoothness. The results of simulations demonstrate that， compared with paths generated by an improved A* algorithm， the proposed method achieves smoother trajectories while maintaining comparable path length and significantly increasing the minimum distance to obstacles， and highlighting its superior safety performance in obstacle avoidance. The research can serve as a practical solution for ensuring safe UAV navigation in complex urban environments.

To address the challenge of significant attitude deviations and persistent oscillations in coaxial unmanned aerial vehicles （UAVs） under wind disturbances， an adaptive backstepping attitude control method is proposed. Firstly， a nonlinear attitude dynamics model integrating with wind disturbances is established through mechanical modeling for coaxial UAVs. Subsequently， a neural network is employed to estimate real-time disturbance amplitudes in the pitch and roll channels for the torque and model uncertainties caused by wind disturbances， while an adaptive backstepping controller dynamically adjusts control parameters for precise stabilization. Finally， the performances of attitude tracking and disturbance resistance of control system are tested through simulations. Comparative simulations demonstrate the adaptive backstepping based method has superior performance over PID control in attitude tracking accuracy and disturbance rejection robustness and significant improvements in overshoot suppression and oscillation attenuation. These results validate this solution in complex disturbance environments for coaxial UAV attitude control.

To address the issues of challenges posed by the resource constraints faced by emergency UAVs equipped with communication base stations， a heterogeneous integration architecture and system-level resource constraints optimization research is proposed. Firstly， based on the characteristics of UAV subsystems， a heterogeneous air-space-ground integrated emergency communication framework is constructed. Secondly， specific performance indexes and functional requirements are formulated under resource constraints， and targeted interference mitigation solutions are developed. Finally， through link budget analysis and field experimental validation， the effective coverage range at the recommended transmission rate is empirically determined. By ensuring UAVs which can "fly farther， see clearer， be reliably controlled and be effectively utilized" during critical moments and supporting the sustainable development of the low-altitude economy， the proposed research has points to achieve the goals.

In this paper， the federated contrastive learning method for SOH estimation of lithium batteries is proposed. Firstly， federated learning is utilized to jointly train a model across multiple clients， which can enable knowledge sharing among clients while data privacy is protected. Next， in the federated learning framework， the concept of contrastive learning is introduced to achieve feature alignment among multiple clients， thereby the data distribution differences among clients can be reduced. Further considering the differences in data quality among different clients， a dynamic weighted aggregation algorithm is proposed to reduce the impact of low-quality data on the global model. Finally， the effectiveness of the model is validated on the 18650 lithium battery data set and data privacy protection and the fairly lower SOH estimation error are both guaranteed.

The evolutionary trajectory of aerospace control technology is focused from classical control and modern control to agent-featured intelligent control technology 3.0. The agent-featured intelligent control technology 3.0 is represented and known as the key indicators of future aerospace control systems. The key attributes of control technology 3.0 labelled by "learning while flying"， "lifelong learning" and the "new-generation system architecture" are pointed to elucidate. The critical technologies of "intelligence empowerment"， "functional augmentation" and "information capability enhancement" are subjected to in-depth analysis. On this basis， the exploration of future aerospace intelligent control development is oriented to typical scenarios such as large model empowerment and software factories. Consequently， prospective thoughts on the development of advanced intelligent aerospace control are expanded upon the matter.

The generation of abnormal time-series data for weapon systems is one of the core challenges in the field of industrial intelligent operation and maintenance. Traditional equipment data generation method suffers from several limitations in abnormal data generation， including strong coupling in the latent space， violation of physical laws and a lack of diverse abnormal patterns. To address these issues， a causal disentangled VAE method is proposed in this paper， which involves latent variable independence through causal graph constraints， incorporates physical equations taken as prior knowledge in the decoder and a dynamic perturbation strategy employed to generate diverse abnormal data and realize equipment abnormal time-series datasets intelligentized generation. Experiments demonstrate that this method takes advantage of fairly good adaptability by single factor accuracy control， fit time dimension fault propagation and physical constraint controllability， and the CD-VAE significantly outperforms existing methods in terms of physical plausibility， interpretability and the training effectiveness of anomaly detection models on multiple cross-domain public benchmark datasets.

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 comprehensive optimization of the robust disturbance rejection capability， convergence time， and control accuracy of traditional UAV cooperative formations， a particle swarm optimization-based fast robust cooperative control method for multiple UAVs is proposed in this paper. A finite-time cooperative formation controller is designed to accelerate the response speed of traditional distributed formations. A fast disturbance observer is developed to compensate for the control system， which is capable of accurately estimating composite disturbances within a finite time， thereby the formation control precision and robust disturbance rejection are enhanced. On this basis， by considering both the convergence time and control error， a penalty-based particle swarm optimization algorithm is employed to optimize the design parameters of the formation system， which comprehensively improves the flight performance of multiple UAVs robust cooperative control.

To address the issue of challenge of rapid and accurate prediction of the trajectory terminal velocity by using offline trajectory optimization methods during unmanned vehicles operation under complex and strong interference， an integrated velocity prediction and control algorithm is proposed， which is based on the improved gated recurrent Unit neural network algorithm. The velocity prediction is based on the parameters of the neural network model trained by a trajectory data sample library， which takes an eleven-dimensional feature sequence including the target position， current altitude， velocity， ballistic angle and other relevant parameters as input of the network and the velocity at the final moment as the output， and yields a neural network model capable of predicting terminal velocity. Based on the velocity prediction results， a decoupling control scheme for velocity and position is employed for terminal velocity control. The predicted terminal state deviations are used to correct and generate closed-loop control inputs for terminal velocity regulation. In final stage， the designed velocity prediction and control method are validated through six-degree-of-freedom （6-DOF） ballistic simulations. The simulation results demonstrate that accurate and effective velocity prediction and control can be relatively achieved by using proposed algorithm applied to the terminal velocity under 6-DOF closed-loop state.

Regarding the online evaluation of the flight capability of aerospace transportation spacecraft， a customized online reachable domain calculation method is proposed. Firstly， a reachable orbit envelope calculation scheme is designed around the current orbit plane under given constraints and initial conditions. Secondly， customized methods and hardware product modules for heterogeneous acceleration are designed， which can quickly achieve online calculation of reachable orbit envelopes and provide fuel optimization planning guidance for specific orbits. Finally， the proposed customized calculation method for reachable domain is validated from the aspects of reachable domain calculation analysis and planning guidance. The results show that the proposed algorithm has good convergence， can quickly calculate the reachable domain envelope and the control variables can smoothly adapt to changes in orbital parameters.

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.

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.

The utilization of drones and other devices for low-altitude inspection is recognized as a typical application scenario in the future low-altitude economy. Infrared imaging， as a critical tool for low-altitude environmental perception， faces challenges in acquiring reliable datasets due to high costs and difficulties in ensuring confidentiality. A style transfer-based target implantation method is proposed to generate simulated infrared images. On the basis of this method， which is initiated by solving the target temperature field through finite element analysis， atmospheric transmission effects are incorporated to render preliminary simulated images. A convolutional neural network-based style transfer technology is then utilized to achieve high-quality implantation between targets and real infrared background images. Comparisons are conducted against traditional methods through three different scenarios. Quantitative evaluations are performed by using objective metrics， including information entropy， peak signal-to-noise ratio， standard deviation， average gradient and spatial frequency. Experimental results demonstrate average improvements by 5.82%， 1.03%， 4.24%， 10.5% and 33.58% of these metrics， respectively. The proposed method is proven to significantly outperform traditional approaches in high-frequency information reconstruction and detail preservation.

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.

A positioning method is proposed for high Earth orbit （HEO） spacecraft based on Chebyshev orthogonal domain transformation. By transforming the time-varying receiver coordinates over a period into an invariant Chebyshev coefficient domain， this approach is based on effective combination with sparse ranging observations obtained by the spacecraft across different epochs， which enables joint resolution of multi-epoch measurements. Under conditions of sparse satellite visibility， the historical observation data is leveraged to provide effective constraints for positioning on the current epoch and achieve continuous and reliable positioning for medium-high Earth orbit （MHEO） spacecraft. Furthermore， the Chebyshev based positioning method demonstrates robustness against random measurement errors during observation， which enhances GNSS positioning precision in high-altitude environments. The experimental and simulation results demonstrate that the method is superior to the Extended Kalman Filter （EKF） by handling random noise and surpasses traditional least squares algorithms in both computational speed and positioning precision， which is capable of continuous positioning for spacecraft by pseudorange-level in high Earth orbit scenarios.

With the rapid development of information technology and the significant increasement in product complexity， the cost of physical experiments is still on the rise， so that​simulation technologies are recognized as critical tool for system design optimization. However， traditional simulation algorithms are challenged by high computational resource consumption， difficulty in global optimization and poor adaptability. Regarding these issues， a general simulation algorithm scheduling platform is proposed and based on deep reinforcement learning， which consists of a task list distribution algorithm based on SAC and a computational task scheduling algorithm based on DQN. Tasks are intelligently distributed to computing nodes through the SAC algorithm to optimize task execution efficiency， and but the autonomous scheduling capability of computing nodes is enhanced by the DQN algorithm through experience classification， thereby improving resource utilization. Experimental results demonstrate that the proposed algorithms achieve superior performance by comparing with traditional methods in terms of both task completion time and resource utilization， which validates their effectiveness and advancement.

Regarding the challenges associated with hypersonic vehicle flight control， such as difficulties in meeting angle-of-attack （AOA） constraints and the tendency for tracking errors to exceed limits， an asymmetric time-varying constraint backstepping control scheme is proposed， which integrates a fixed-time sliding mode disturbance observer with prescribed performance control. Firstly， the longitudinal dynamic model of the hypersonic vehicle is established， and the velocity and altitude subsystems are identified. These subsystems are then transformed into strict feedback models for backstepping controller design. Subsequently， by combining prescribed performance control with an asymmetric barrier function， a novel control scheme is designed to ensure that the AOA error remains strictly within a predefined time-varying range. Additionally， a fixed-time sliding mode disturbance observer is introduced， which guarantees that the disturbance estimation error converges within a fixed time and ensures the robust performance of system under external disturbances. The simulation results validate the effectiveness of the proposed control scheme which is compared with traditional control methods， and demonstrates superior performance in terms of AOA constraint satisfaction and tracking precision.

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.

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.

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.

Regarding the geosynchronous orbit （GSO） multi-target flyby imaging services mission， a two-layer mission planning method based on a novel orbital configuration is proposed. Firstly， orbital configurations and maneuver strategies applied to existing research are analyzed， and a frozen-high elliptical orbit （F-HEO） is proposed for the mission. Based on J ₂ dynamics model， considering with temporal and positional consistency constraints， the perigee double-impulse maneuver strategy is studied. On the basis of this， the mathematical model for the inner-layer single-target maneuver planning and the outer-layer multi-target sequence planning are established. A dynamic neighborhood search （DNS） is adopted in the inner-layer to accelerate search， and the outer-layer employs a genetic algorithm to obtain the global optimum. Finally， a simulation case is implemented by considering with typical GSO targets.Results verify the feasibility， rationality and accuracy of the mission planning method and show that DNS can effectively reduce the time consumption of inner-layer optimization computations and the F-HEO takes advantage of lower fuel and time costs.

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.

According to liquid sloshing control with pole-zero structure，a method using disturbance compensation is proposed in this paper.The total disturbance torque is estimated by using the extended state observer（ESO） and used as a control command.Based on the typical dynamics model of launch vehicle，the transfer function of the rocket body is derived，and the relationship between the sloshing structure of pole-zero and the disturbance compensation torque is obtained. Though the engineering application， suggestions on the use of the method is presented，and the feasibility of method is proven by a calculation instance，which can serve as reference for stability control of liquid sloshing.

Aiming to the problem that the spin modulated inertial navigation system carried by aerospace vehicles is difficult to achieve accurate initial alignment for short time during cold start up， a new alignment method based on recursive least squares（RLS）and multi-position alignment is proposed. Firstly， this method is based on the velocity error analysis of the inertial navigation system， and the correlation equation is deduced between the error velocity with the attitude misalignment angles and the zero offset of inertial measurement units to realize the error corrections of the inertial navigation system. Next， RLS estimation and iterative optimization are used to analyze the initial target alignment error parameters. Finally， it is verified by simulation experiments and physical experiments. The results show that in the case of cold start in a short time， compared with the traditional Kalman filter alignment algorithm， the initial alignment precision of the azimuth angle of the inertial navigation system can be significantly improved and the average error standard deviation is reduced by about 42.7%.

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 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.

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.

Regarding close-proximity explorations near asteroids， a collision avoidance low-thrust trajectory optimization method is proposed， which integrates Flipped Radau pseudospectral approach with convex optimization. A four-mass gravitational field modeling method is introduced to enhance computational efficiency while precision is maintained in gravitational field calculations. The dynamics is discretized by using the Flipped Radau pseudospectral method and the ellipsoide constraint is imposed to avoid collision， then the trajectory optimization issue is proposed. By applying convexification techniques to reformulate non-convex terms in the issue， and a pseudospectral convex optimization framework is established. The optimal trajectory is obtained by iteratively updating the nominal values until convergence. The collision avoidance is enabled in low-thrust transfer trajectory optimization among equilibrium points near asteroids by using the proposed method. It demonstrates favorable convergence performance and computational efficiency in complex asteroid exploration scenarios， thereby providing theoretical and technical support for asteroid explorations and other deep space exploration missions.

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 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.

According to thrust failure during the orbit insertion phase of a launch vehicle beyond the atmosphere， a trajectory replanning method based on a multi-operator differential evolution algorithm is proposed. On the basis of traditional differential evolution algorithm， chaotic mapping and multi population parallel computing are introduced to improve the slow calculation speed and easy getting stuck in local optimal solutions of traditional methods during dealing with trajectory planning problems. At the same time， a decision vector processing mechanism is specifically designed to address the issues of disorder and repetition of time variables. The experimental results show that the algorithm proposed outperforms the standard deviation evolutionary algorithm in both convergence accuracy and running time. And by comparing with the pseudo-spectral method and iterative guidance algorithem， the effectiveness and accuracy of this algorithm proposed in trajectory planning are further verified， hereby demonstrating its potential for industry applications.

A many-to-many rapid rendezvous method based on two-impulse planning is proposed for scenarios involving a spaceborne mother platform deploying multiple sub-satellites for fast rendezvous to engage with multiple target spacecraft. Firstly， a semi-analytical solution method for the single-impulse reachable domain of mobility-constrained spacecraft is presented， which significantly reduce computational complexity. By ensuring the mother platform's reachable domain that covers the target orbit， the required impulsive velocity magnitude is calculated， while the optimal impulsive velocity direction is determined by minimizing the future relative distance between the mother platform and the target spacecraft. Subsequently， a genetic algorithm is employed to optimize the pulse sequence of the mother platform by taking fuel efficiency as the performance metric， and after mother platform maneuver is obtained， the orbital deployment positions is yielded for sub-satellites post-maneuver. Finally， regarding the purpose of shortest time for each sub-satellite， Lambert maneuvers based method is utilized to compute orbital transfer points and target track rendezvous points， which enables rapid multi-target rendezvous. The simulation results demonstrate that the proposed method allows the mother platform to minimize fuel consumption while ensuring the sub-satellites achieve rapid rendezvous with multiple target spacecraft under the shortest time conditions.

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.

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 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.