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.

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.

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.

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 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 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.

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.

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.

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.

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.

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.