In order to achieve formation flight for unpowered aerial vehicles and ensure the convergence of formation errors in three-dimensional space， a formation control method based on consensus theory is proposed. Firstly， by taking into account the formation group's characteristics of only having negative acceleration， the consensus theory is modified， and a longitudinal control method suitable for unpowered aerial vehicles is introduced. Next， a bank angle distribution and flipping strategy is involved， and under the condition that the lift is prioritized to satisfy high-directional control requirements， the bank angle flips by the lateral error corridor， thereby both high-directional and lateral errors are simultaneously reduced. Finally， simulations are conducted to verify the adaptability of the proposed method to various formation sizes and communication topologies. The simulation results demonstrate that， within the given range of initial conditions， the proposed method can effectively control the formation in different initial states. Moreover， the initial state of the cluster has significantly impacts on formation time and cluster speed loss and average formation time and lower average cluster speed have less loss while there are clusters with more directed edges in the topology structure.
On account of the issue that the communication network of multi-flight vehicles is vulnerable to be attacked by false data injection in the disturbance environment， a collaborative guidance strategy against high-speed targets that can autonomously identify and suppress the fault link is proposed. By designing the coordinated guidance method based on relative distance， when only the leader flight vehicle can get the target movement information， the flight vehicles can hit the maneuvering target at the specified time. The radial basis network is used to estimate the unknown items for the design of cooperative guidance law. The trust coefficient is introduced to identify the false data injection attacks on the communication network， which guarantees the information assurance for self-suppression of the multiple flight vehicle systems under the network attacks.The numerical simulation result indicates that the leader and followers multi-vehicle collaborative impact on the high-speed target simultaneously can be controlled and implemented by using the proposed method under the false data injection attacks.
Aiming at the obstacle avoidance of multiple quadrotor UAVs in complex and variable wind fields， a aimed trajectory planning algorithm based on Gauss pseudo-spectral method （GPM） is proposed to solve the obstacle avoidance of multiple quadrotors in mixed wind fields. Firstly， a dynamic model for a single quadrotor in a mixed wind field is established. On the basis of this， the constraints on the wingmen in the formation are transformed into constraints on the leader， thereby a multi-constraint trajectory planning model is established. Under the premise of ensuring that the formation can reach the target location on time， this model minimizes the energy consumption required for formation flight is minimized by using this model regarding the optimization objective. The GPM is utilized to optimize the trajectory of the leader， which is then formulated as a nonlinear programming problem （NLP）. The sequential quadratic programming （SQP） algorithm is adopted to solve this problem， which yields an optimal flight trajectory that satisfies both the constraints and the optimization objective. Simulation results demonstrate that the proposed method enables multiple UAVs to safely navigate around obstacles within mixed wind field environments and generate optimal flight trajectories while satisfying the constraints.
Aiming at the stability control of a slender air defense missile under low elastic frequency and multiple constraints， a linear time-varying model predictive control method which is considered under elasticity and multiple constraints is proposed. Firstly， in order to ensure the amplitude attenuation in the mid-frequency band， the overload error integral output is used to augment the missile's linear state space equation. Under the model predictive control full state feedback strategy， the controller inherits the structure of the traditional three-loop control method. Then， a cost function is established， which is based on the criterion of ensuring the rapidity and smoothness of the missile's overload and attitude response， and the missile's stability control problem is transformed into a rolling optimization solution of model predictive control. Finally， in order to ensure the stability and elastic suppression capability of the system under elastic working conditions， a notch is inserted into series at the output end. The simulation results show that the proposed method has faster response speed， higher control accuracy and better elastic suppression ability， compared with the traditional three-loop control method.
An adaptive sliding mode control strategy based on the randomized feedforward neural network is proposed for the path tracking control problem of heavy-load quadrotor unmanned aerial vehicle （UAV） affected by model uncertainties and external unknown disturbances. The dynamics system of heavy-load quadrotor UAV is experienced by the model uncertainties and external unknown disturbances during flight which are defined as the lumped disturbance term. By taking advantage of randomized feedforward neural network， the lumped disturbance term in each channel is adaptively estimated and then used to compensate the adaptive sliding mode controller. Based on the Lyapunov stability analysis method， a rigorous proof of the convergence of path tracking errors for heavy-duty quadrotor UAV is presented. The effectiveness of the proposed control method is fully verified by the simulation results.
A combined navigation positioning method based on improved radial basis function neural network （RBF） assisted volume Kalman filter （CKF） is proposed to resolve reduced precision of integrated navigation positioning caused by global navigation positioning system （GNSS） signal interruption in complex environments such as tunnels， urban roads and canyons. Firstly， the integrated navigation fusion data is preprocessed by using kernel principal component analysis （KPCA） combined with K-means++ clustering model to make its distribution representative； Secondly， the orthogonal least squares （OLS） method is used to determine the number and center values of hidden layer neurons in the RBF neural network， and the trust region constrained Gaussian-Newton （TR-CGN） algorithm is used to optimize its parameters； Finally， when the GNSS signal loses lock， the trained improved RBF neural network is used to assist in nonlinear CKF filtering for error compensation. The experimental results show that the average positioning error is reduced by 17.87% through application of this method without increasing hardware costs which is compared to the way of using the autonomous driving collaborative positioning system； Compared with the average positioning error assisted by KPCA-RBF， the reduction based on the proposed method takes advantage of 54.37%， which indicates that the adaptability and robustness of the integrated navigation positioning system are effectively enhanced within complex environments.
A hybrid optimization method based on deep neural networks is proposed in this paper for the spacecraft pursuit-evasion game problem with free terminal time and perturbation considerations. Firstly，the data set is generated by solving the two-point boundary value problem without perturbation using traditional optimization algorithms. Then， on the basis of that， a deep neural network is established to fit the relationship between the initial state and the solution， and the initial guess solution is generated. Finally， the solution is further optimized by using a local optimization algorithm. Through simulation and verification， it is demonstrated that this method not only performs good feasibility and robustness but also significantly improves computational efficiency， which is compared with traditional hybrid optimization algorithms.
Aiming at the shortage of existing fault diagnosis methods for in-orbit satellites， a satellite fault diagnosis system based on Clips expert system is proposed in this paper. The satellite fault is diagnosed in real time by taking advantage of existing satellite fault knowledge and experience， combining expert experience with real-time telemetry data and using inference machine technology. At the same time， expert knowledge is input and edited by visualization technology including graphics and knowledge expression. The expert knowledge base is established to transform the simple logic statements that is easy to be understood by users into complete and complex Clips statements to realize complex fault diagnosis programs， and it is going on to realize the automation and intelligence of real-time fault diagnosis of spacecraft in orbit， accurately locate faults and improve the reliability and safety of satellite systems. Through multi-domain simulation of multiple fault scenarios and project practice， the diagnosis results of the satellite fault diagnosis system are the same as the actual fault data received by the satellite， which verifies the effectiveness of the designed system.
Aiming at the current situation of tight software-hardware coupling， large scale and high complexity in the test and launch control system of aerospace equipment， a layered decoupled software-defined test and launch control system architecture is proposed. Through taking advantage of middleware technology， the hardware resources of test and launch control system are highly integrated and abstracted， and application-oriented software is enabled to dynamically load and reconstruct components based on users' requirements. The proposed architecture has the features of hardware-on-demand expansion and flexible high extensibility of software， which improves the utilization rate of test and launch control system resources， task flexibility and system reliability.
As a critical factor affecting safety-critical systems， it has been drawn increasingly attention to software safety issues. Based on engineering practices in aerospace embedded software testing and verification， the software safety requirements are taken as a clue and typical safety problems are focused in this paper. The two dimensions are introduced by the formal verification of source code safety properties and the automated testing of software safety requirements which analyze and summary key technologies， including special safety analysis， source code static analysis， source code model checking， fault-model-based safety testing and keyword-driven automated testing. A comprehensive technical solution for aerospace embedded software safety verification is proposed， and an independent controllable software assurance support platform and tools are developed to systematically enhance the trustworthy assurance capabilities of aerospace embedded software.
Aiming at full system test data and career information， environmental data， etc of multiple source and huge heterogeneous data of aerospace equipment， a data-based health management system is developed，which achieves the integration and storage management of test data，product career data，environmental data， model analysis and health assessment.The system is developed through service-oriented architecture design， which is based on information gathering and processing for realizing integration and management of equipment test data and takes advantage of high-reliability test data storage and management foundation using lightweight distributed column storage mechanism.Regarding the health status of the system and key units， the trend analysis，life prediction， maintenance decision information and hierarchical situation presentation， which are based on algorithm models，are introduced. Realization of the health status evaluation，health trend analysis，life prediction，maintenance decision support，etc for the full system and key units can be served as a reference of equipment data health management solution.
Regarding the focused security requirements of test launch and control system in authentication and authorization， data security protection and traceability of abnormal operations， researches are conducted on the application of blockchain technology in authentication and authorization management. The design of network architecture， account information model， consensus mechanism and differentiated authorization smart contract algorithm are elaborated in this paper， which is verified through the simulation by using self-developed measurement and control network blockchain authentication and authorization platform.
