The development of model-based systems engineering (MBSE) and its application in aerospace control are reviewed. Firstly, the definition and development process of MBSE are introduced, and the three pillars of MBSE are introduced, including modeling language, modeling method and modeling tool, and the characteristics of mainstream modeling tools are compared. Secondly, the existing researches on the application of MBSE in aerospace control are reviewed from three aspects: control system architecture design, control system reliability and safety analysis, and control system verification and validation. Finally, the application prospect of MBSE in aerospace control is presented by focusing on multi-domain co-simulation, control system digital twin and intelligent design and optimization of control system.

Inertial measurement unit (IMU) is an important component of anti-aircraft missiles and its state is mainly attained from periodical manual testing, which is not effcient. Aiming at reducing the dependence on periodical testing, time series prediction methods are developed to predict part of the states of IMU by processing existing data. Under the consideration of the small sample size, overlapping segmentation averaging is used for data processing to reduce feature dimension and training difficulty. Long short term memory network (LSTM) is used for subsequent time series prediction. The effect of the proposed model is validated through practical data, which attains a high prediction accuracy while taking little time consumption.

In order to achieve the measurement of unknown space optical environments for star sensors, the design of a wide and multi-spectral filter device integrated into the star sensor is proposed in this paper. The star point information and the spectral information of the space optical environment can be covered by the collected star images. On the basis of this process, experiments scheme of spectral response and radiation calibration and algorithm of brightness distribution inversion of the optical environment are conducted. The experimental results show that the inverted brightness of the optical environment is in good agreement with the measured values. The space optical environment for star sensor applications in real time can be estimated by applying this method conveniently based on a single star image. It can be served as a basis and reference for design optimization of subsequent star sensors and selection of observation windows.

Regarding the controller design of the variant vehicle attitude control system, an integrated controller design method based on structure search and parameter optimization is proposed, which can realize the automation of the controller structure design work, searches for the most suitable controller structure and completes the parameter rectification in a shorter time. The simulation study on the attitude control of the pitch channel of a certain type of variant vehicle shows that the method can significantly improve the bandwidth of the system, and the number of iterations and the consumed time are both promoted by only about 38% than those of the traditional method.

A differential games guidance law is proposed for the difficulty of intercept of hypersonic vehicle. Firstly, the model of air confrontation is established between friend and foe in three-dimensional space and the optimal performance indexes are proposed. The optimal strategies are resolved according to Riccati Equation updated in real-time that is based on state-dependent coefficient. In order to solve Riccati Equation, more accurate Schur method and approximate calculation that has but faster θ-D method are applied to obtaining two kinds of differential games guidance law. In the operational scenario of high speed serpentine maneuvering flight target, traditional proportional guidance and differential games guidance law are analyzed by using the simulation results. Compared with the performance, a conclusion is drawn that in face of high speed maneuvering target, the effect of differential games guidance law is superior to proportional guidance.

Aiming at the problem that the displacement stiffness parameters of a magnetic suspension flywheel are more prone to generate perturbation than other parameters, the sensitivity of the eigenvalues to the displacement stiffness is optimized under the constraint that the closed-loop state matrix is a normal matrix, and the full state feedback controller parameters are solved based on the optimal eigenvalues. In order to achieve full state feedback control for stateless observers, the control model is extended to a non-minimum state space, and all state variables can be directly calculated from the input and output quantities of the control object. In order to further improve robustness, the equivalent disturbance signal compensation method is applied to the optimized control system. The simulation shows that when the displacement stiffness perturbation reaches 70% of the nominal value, the closed-loop system with state augmentation can still stabilize the system, and has better adaptability to the displacement stiffness perturbation and shorter adjustment time; The dominant eigenvalues of the closed-loop system closest to the virtual axis are further away from the virtual axis, and the controller is more adaptive to parameter perturbations, but the energy cost of control is greater; The adaptability of the controller can be improved to parameter perturbation without changing the dominant eigenvalue by using the signal compensation.

In this paper, the problems of missile formation control in the process of external disturbances, input saturation and unknown obstacles in route planning are researched. Firstly, based on the missile nonlinear dynamic model, the absolute error control model of the missile formation is established by defining the position and speed error auxiliary variable under involving external disturibances. Secondly, on the basis of the nonlinear sliding mode surface based on obstacle avoidance potential function and hyperbolic tangent function, the controller for adaptive anti-saturation and robust missile formation obstacle avoidance is designed by applying adaptive technology and anti-saturation auxiliary system. Finally, Lyapunov stability theory is used to prove that the performance of the designed system is asymptotically stable, and the effectiveness of the designed control strategy is verified by digital simulation.

In order to improve the accuracy and autonomy of aircraft navigation system, a gravity gradient/SINS/starlight integrated navigation method is proposed. In this integrated navigation system, the gravity gradient information is used to correct the position error of SINS, and the starlight information is used to correct the attitude error of SINS to improve the navigation accuracy and autonomy of the aircraft. Regarding overcoming the shortcomings of the traditional cubature Kalman filter algorithm(CKF) which has low accuracy, the random weighted cubature Kalman filter(RWCKF) is used to design the gravity gradient/SINS/starlight integrated navigation system. The simulation results show that the position errors of SINS/starlight integrated navigation system, gravity gradient/SINS integrated navigation system, SAR/SINS/starlight integrated navigation system and the proposed method are 78.1003 m,54.3399 m,39.2776 m and 19.8495 m respectively, which prove that the accuracy of the proposed integrated system is much higher than not only the two subsystems but also the SAR/SINS/starlight integrated navigation system.

In order to avoid the huge workload caused by repeated iterations in the process of guidance and control system design, a three-dimensional integrated guidance and control design under impact angle and rudder surface saturation constraints is proposed, which is more applicable in practice. Based on the 6 DOF nonlinear model of missile and the 3 DOF missile-target relative motion model, an integrated guidance and control model with full-state coupling is established. Based on the dynamic surface control method, a four-stage sliding surface design is applied to establish a controller which satisfies the impact angle and rudder surface saturation constraints, and the closed-loop stability of the system is proven. The correctness, effectiveness and robustness of the proposed method are effectively verified by simulation results.

The characteristics of existing project test data in the aerospace field are analyzed and then the results show that the traditional relational database can no longer meet the storage and management demands of ever-increasing massive test data. Aiming at the characteristics of large magnitude, low write throughput and poor access efficiency of existing test data, a test data storage management method based on time series database is proposed. By comparing and analyzing the reading and writing performance of aerospace project test data in traditional relational database and time series database, it is verified that the performance of data writing and data query based on time series database is obviously better than that of relational database, and it has the capabilities of fast saving and fast accessing. This method is applied to test data management system and data processing display and analysis system, which can meet the requirements of real-time writing and fast reading of test data processing.

Aiming at the collision rendezvous scene between the space vehicle and the potentially threatening object, a maneuvering and evading strategy of the space vehicle is proposed, which is based on genetic algorithm optimization and BP neural network prediction. By presenting the difference against the traditional maneuver strategy in the last phase of collision rendezvous, the evading strategy is selected after the warning and detection by the proposed strategy algorithm that expands the scope of time and space and takes the position and speed at both sides as input parameters, the time, direction and duration of maneuver as output parameters, miss distance and fuel consumption as performance indexes. The numerical simulation shows that the proposed strategy effectively reduces fuel consumption, increases miss distance and improving the possibility of successful evading, which is compared with the instantaneous maneuver strategy and provides a new idea of safety protection technology for space vehicle under security threat.

Regarding the stringent requirements of information confidentiality review in the aerospace field, current manual screening methods are suffering from high costs and insufficient accuracy of keyword matching. An enhanced review framework integrated with large language models is proposed to improve the efficiency and accuracy of confidential information screening. Initially, the characteristics of confidential information are analyzed in the aerospace sector, an architecture that enhances the auditing performance of large language models is introduced in this study, which is combined with dynamic domain-specific expert system prompts to enhance the granularity and accuracy of reviews among multiple perspectives including technical and business confidentiality. By introducing a dynamic system prompt mechanism, the framework is effectively combined the semantic understanding capabilities of large language models with the real-time updating of keywords. Additionally, in order to prevent excessive auditing by the large language model, a hybrid cross-training strategy is developed, which significantly improves the recall rate of confidential information that reaches by 96%. Experiments on a self-developed high quality test set of 1000 entries demonstrates that the proposed method outperforms global open-source large language models by 18% in aerospace classified information inspection tasks.

Aiming at using the attitude control scheme of taking rotator as ball-borne gondola, stepper motor is applied as the power source and a cascade controller based on active disturbance rejection control (ADRC) algorithm is designed, and the key parameters in the controller are optimized by using dung beetle optimization algorithm (DBO). The optimized controller parameters are then used to simulate the attitude control of the gondola, considering dropping ballast during the flight. The simulation results show that the proposed control algorithm can track static and dynamic targets with better control accuracy and anti-interference ability. Furthermore, the effectiveness of the DBO is also confirmed.

In order to solve the guidance problem of glide vehicle under terminal velocity constraint, two guidance methods are proposed after analyzing the deceleration effect of lateral overload in this paper. Firstly, a lateral maneuvering model is established and the effect of lateral maneuvering motion on energy dissipation of the aircraft is analyzed, and then using lateral maneuvering overload to control the terminal velocity is proposed. Secondly, in order to accurately control the terminal velocity, guidance methods based on flight speed correction and neural network are proposed respectively, and six degrees of freedom simulations are implemented. The simulation results show that the guidance methods proposed in this paper can accurately control the terminal velocity of the aircraft under the constraint of impact angle. The guidance method based on flight velocity correction is easy to implement in engineering practice, and the guidance method based on neural network has better control effect and the ability to deploy online. The terminal velocity constraint guidance methods proposed have good engineering applicability and have reference significance for the guidance system design of this kind of glide vehicle.

A designed method is proposed in this paper for the parameter adjustment of three-loop autopilot with pseudo-angle. Ulteriorly, regarding the open-loop performances indexes of the control system, the stabilizing and the overload loops are successively disconnected at the rudder control, and the relationship is established between open-loop parameters and the three-loop autopilot parameters with pseudo-angle. Then, by taking the open-loop performance parameters as independent variable and the closed-loop performance parameter as optimization goal, particle swarm optimization algorithm structure and boundary conditions are designed, and the quickly parameters adjustment of three-loop autopilot with pseudo-angle is realized. The simulation results show that good open and closed loop performance of three-loop autopilot can be achieved through the designed method based on particle swarm optimization.

A multi-channel and end-to-end attitude control method based on deep reinforcement learning is proposed for hypersonic morphing vehicle in the presence of situations by external disturbance and model uncertainty. Firstly, the attitude control model of hypersonic morphing vehicle is established. Secondly, the problem of vehicle attitude control is transformed into a Markov decision process. Furthermore, the training of the agent is implemented, which is based on the twin delayed deep deterministic policy gradient algorithm, and the end-to-end generated flight control instructions are deployed online. Finally, the proposed method's effectiveness and generalizability are confirmed through both basic performance and adaptive simulations.

Aiming at the strong coupling, nonlinearity and severe aerodynamic parameter variations under the complex environment of vehicles, an active disturbance rejection controller design method based on the dragonfly algorithm is proposed. Firstly, a mathematical model based on the three-channel powerless reentry process of vehicles is established. The extended state observer is used to estimate the system state and total disturbance, and then the observed disturbance is compensated. The dragonfly algorithm is employed to optimize the parameters of the active disturbance rejection controller. Finally, validation is shown through a vehicle simulation of six-degree-of-freedom. The results show that the optimized controller parameters have better control accuracy and maintain good control performance even under significant aerodynamic parameter deviations.

A pulse threshold relative orbit control method is proposed to address the issue of maintaining the formation configuration of inner formation satellite in case of collision due to the perturbation difference between internal and external satellite. A periodic-start pulse threshold controller is designed, and the applied pulse impulse is calculated based on the relative position and threshold of the inner and outer satellite centroids as well as the linearized relative orbital motion dynamics model. The Monte Carlo method is used to simulate the state estimation bias of the inner and outer satellite,which is compared with two continuous control methods under the same conditions. The results show that a good balance can be achieved by using this method regarding control accuracy, fuel consumption, and the influence of measurement error, which can effectively reduce the uncertainty caused by state deviation on fuel consumption and can be applied to the maintenance of formation configurations of inner formation satellite with low fuel consumption in orbit for a long time. The proposed method can also be applied to the maintenance of general formation satellite configurations and planetary companion flights.

In order to realize the compliant docking of the spacecraft and the solar wing, a contact force control strategy based on impedance control is proposed in this paper, and the six-degree-of-freedom parallel attitude adjustment mechanism is token as research purpose. Firstly, the impedance model between the docking member and the member to be docked is established, and the contact force error quantity is converted into the position correction quantity. Secondly, based on the impedance control, an adaptive controller is introduced, which solves the disadvantage of the fixed target parameter of the impedance control and improves the adaptability of the control system to the environment. At the same time, a fuzzy controller is incorporated to adjust the impedance parameter on-line in real time. Finally, a joint simulation is implemented, which verifies the effectiveness of the proposed method by the experimental results based on combining Adams with Simulink.

Aiming at the multi-constraint and strongly time-varying problems of high-speed aircraft during re-entry gliding, this paper combines the online autonomous decision-making advantages of the Deep Deterministic Policy Gradient (DDPG) algorithm to generate avoidance strategies in real time based on threat zone information for dynamic no-fly zone avoidance trajectory planning. In order to further enhance the anti-interference ability of high-speed aircraft to environmental uncertainties, a set of route feature points is selected based on the avoidance trajectory, and a online predictor-corrector guidance method is used to correct the flight status of the high-speed aircraft in real time according to flight mission requirements and terminal constraints, and finally achieve precise guidance of high-speed aircraft. At the same time, in order to verify the effectiveness of the method, this paper carried out corresponding numerical simulation analysis. The results show that the method proposed in this paper can effectively avoid no-fly zones and enhance the adaptability to uncertain factors, and has certain engineering application value.

A trajectory planning method is proposed for morphing aircraft with threat zones overlapped and full coverage of flight paths, which involves the probability of passing through the threat zone and optimization of variable shape parameters. Based on the idea of hierarchical reinforcement learning, a hierarchical reinforcement learning model for route decision of morphing aircraft is established by configuring the flight environment set, decision options, cost function, Q function and strategies within the options. By training the evaluation network, it can make route decisions for actual scenarios based on the probability of passing through the threat zone. According to the characteristics of the variable shape of the aircraft, the obtained decision results are optimized by parameters to obtain the full process travel trajectory and aircraft shape. The simulation results show that this method can make real-time flight route decisions based on actual situations, and the trajectory and the flight form in overall procedure can be obtained after optimization.

In order to solve the low fault diagnosis accuracy of aircraft rudders under complex and dynamically changing operating conditions, an NDTL-CNN fault diagnosis method that combined convolutional neural network (CNN) with network-based deep transfer learning (NDTL) is proposed. Firstly, a simulation model of aircraft rudder fault is established to collect multi-dimensional sensor data under different operating conditions and health states; Then, a CNN is designed to adaptively extract deep features from the fixed operating conditions data, which can effectively capture the fault feature signals of the rudder; Finally, the pre-trained CNN under the fixed operating conditions is fine-tuned and transferred to variable operating conditions for fault diagnosis. The experimental results show that the proposed method improves the diagnostic accuracy of the CNN under variable operating conditions by 15% in a short time and the final diagnostic accuracy of the NDTL-CNN reaches 97.7%, which is capable of accurately recognizing the rudder's health state under complex and dynamically changing operating conditions.

The guidance law of air-to-air missile with large off-axis launch angle is studied in this paper. The BTT control mode is applied to missile cruise in the turning stage. The mechanism and requirements of fast turning of missile are briefly analyzed, and the optimal lift surface for fast turning is studied and the missile roll control command is determined. The dynamic model of missile is established in turning phase, and the control command of angle of attack is designed by using the relation of residual turning angle. The rotation coordinate system is proposed to optimize the roll control command, solve the problem of command jump and achieve the minimum roll deviation control in the turning process. The projection of the best normal vector of lift surface is used to determine the control logic of the command of angle of attack and realize matching with the minimum roll deviation control commands. In the end, the correctness and effectiveness of design method are verified by digitalized simulation of typical scenes.

In order to cope with the sudden change of the internal forces between both manipulators and the target due to its unexpected maneuvering, a dual-arm coordinated compliant controller for maneuvering target is proposed. Firstly, the open chain dynamics and the closed chain dynamics of dual-arm space manipulators are established, respectively, for dual-arm coordinated compliant operation tasks. Then, by taking into consideration the disturbance caused by the unexpected maneuvering of the target, the autonomous adjustment strategy for the impedance parameters is designed, which can ensure the smoothness and safety of the dual-arm coordinated operation tasks in the case of significant changes of the internal forces between both the manipulators and the target. Finally, a physical simulation experiment system is deployed for dual-arm coordinated compliant operation, and an experiment is implemented, which shows the performance of the proposed dual-arm coordinated transfer controller.

Regarding the issue of high angular velocity damping in satellites, a velocity damping strategy using attitude control thrusters is studied, which is token into account the design principles of fewer startup times and longer startup times for attitude control thrusters. A collaborative damping strategy is proposed, which combines the three-axis simultaneous damping method with the single axis sequential damping method. When the angular velocity of any two axes of the satellite exceeds the set angular velocity threshold, a Schmidt trigger is used for three-axis simultaneous damping to achieve effective damping of the satellite's large angular velocity. On the contrary, single axis damping is used in order of angular velocity to reduce the number of times that the thruster is turned on or off. The numerical simulation results show that the collaborative damping strategy can effectively achieve satellite high angular rate damping and effectively reduce the number and duration of thruster startup.

Regarding the real-time processing requirement of the aerospace model systematic high-bandwidth intelligent bus data and the time-consuming and complex operation and maintenance matters of the existing architecture data processing, a lightweight and highly reliable distributed processing architecture is studied. In order to clean and transform data, metadata model libraries and data parser are built to replace the original Hbase distributed column storage scheme with distributed data store based on ORC file format and to enhance the usability and reliability of the platform; Aiming at enhancing the computing and analysis capabilities of the platform based on the Spark memory computing model, the data stream fragmentation storage mechanism and the index library are combined to optimize and accelerate the data query. The distributed processing architecture has been applied to certain equipment system, which ensures the fast processing, stable storage and high reliable application of the system high density test data and provides a solution for high bandwidth real-time data processing and management.

Improving the accuracy of trajectory prediction is one of the difficulties faced by anti-high-speed strong maneuvering targets. The tracking motion model is established, which is based on the analysis of typical target motion characteristics in this paper. And the interaction multi-model(IMM) filtering algorithm is adopted to complete multi-model interaction. The local filter adopts adaptive high-order cubature Kalman filtering(HCKF) algorithm, and the strong tracking filtering(STF) algorithm is combined with HCKF algorithm to improve the estimation accuracy of traditional CKF and the robustness of the algorithm. The prediction extrapolation method is designed, which is based on the generalized regression neural network(GRNN),and the prediction correction is introduced in the prediction process to correct the error, and the accuracy of long-term prediction is improved by sample learning. The simulation results show that the forecast accuracy of the proposed tracking prediction algorithm is much improved by the compared conventional algorithm.

The sequential convex optimization method applied to the powered vehicle entry trajectory optimization is researched in this paper. Firstly, in order to solve the problem of high coupling and non-linearity of control variables in the original optimization, the linear state equation about control variables is obtained by choosing new control variables due to the relationship between original and new control variables established. Secondly, the nonlinear dynamic equations, performance index and constraints on the path and control variables in the original optimization occurrence are relaxed and linearized. Next, aiming at ensuring the validity of relaxation and linearization, the trust-region constraint is applied to the state variables, and the integral term of velocity azimuth is added to the performance index. Furthermore, the original problem is converted to a convex optimization problem with discretization technique. Finally, the algorithm is proven to be effective by numerical simulation. Under the original nonlinear constraints satisfied, the proposed algorithm is proven to be reasonable with high solving accuracy for the powered vehicle entry trajectory optimization, and the optimized solution can be used as a feasible approach to the original problem in the range of defined precision.

Firstly, the design method of solar synchronous orbit and the calculation method of the local time of descending node are developed, and then the deviation of the local time of descending node is taken as the evaluation standard. The orthogonal experiment design method is used to analyze the influence of orbit injection accuracy on the evaluation standard. The calculation results show that orbit injection deviation can cause the drift of the local time of descending node with time accumulation, the orbital inclination deviation has the greatest influence on the local time of descending node, and the influence of the semi-major axis deviation is taken the second place, while the eccentricity deviation is the least.

The autonomous navigation algorithm during continuous low-thrust orbit control for high-orbit satellites with electric thruster is researched. In view of the relatively poor speed measurement precision of GNSS receivers in high orbit, and the small acceleration generated by electric thruster during orbit control can not be measured accurately by conventional accelerometers, which leads to the decline of autonomous navigation accuracy during orbit control, an integrated autonomous navigation algorithm based on EKF + PI filter is proposed. Firstly, the EKF filter is designed to suppress the high frequency noise of positioning data generated by the GNSS receiver. Then, the acceleration generated by the electric thruster is estimated by PI filter and fed back to the EKF algorithm. By combing the EKF filter with PI filter, the precision of the navigation algorithm is improved during the electric thruster’s working. Lastly, the mathematical simulation is implemented, and the result shows that the integrated autonomous navigation algorithm based on EKF + PI filter can accurately estimate the acceleration magnitude generated by electric thruster. Meantime, the integrated algorithm proposed can effectively improve the precision of autonomous navigation during continuous low-thrust orbit control.

A geomagnetic model error prediction method based on artificial neural networks is proposed in response to the difficulty of improving geomagnetic navigation precision because of the slow update and low accuracy of the geomagnetic field model. The mapping relationship is combined with the magnetic field measurement data of on-orbit satellites to estimate and predict the geomagnetic field model error by establishing the relationship among geomagnetic field intensity vector elements, geographical features, and time information. In addition, a geomagnetic navigation method combining neural network with filter is proposed. In order to verify the accuracy and effectiveness of this method, simulation verification is conducted by using satellite measured information. The results show that compared with the advanced filter method in recent years’ literatures, the position and velocity accuracy can be improved from 4.15 km, 4.38 m/s to 1.34 km, 1.47 m/s that significantly improves the precision and effect of geomagnetic navigation.

According to analyzing the maximum aerodynamic load deviation within 3.5 h between December 2014 and December 2019, the maximum positive deviation reaches 1297.62 Pa·rad and the maximum negative deviation reaches -924.43 Pa·rad, it is proven that it is mainly caused by abnormal increase and decrease of upper wind within 3.5 h. By modeling the upper wind 3 h before launch in the two cases, the absolute difference decreases from 1297.62 Pa.rad and 924.43 Pa·rad to 908.60 Pa·rad and 286.56 Pa·rad, and respectively fallen range to 389.02 Pa·rad and 637.87 Pa·rad. It shows that the revised modeling method has a certain improvement effect, which benefits the guarantee capability of rocket safe flight.

Aiming at the inertial navigation errors rapid increasing in the distributed clusters under the scenario that GNSS signal is unavailable, a collaborative navigation method is proposed in this paper, which just includes total nodes fitted with distributed cluster structure of inertial navigation systems (INSs). A rank-defect constraint Kalman filter is designed to perform collaborative navigation and reduces the errors of the INSs, even if the number of the measurement variables is less than the number of the state variables in the filter. The flight test platform of unmanned air vehicles (UAVs) is established to prove the cooperative navigation system. The results of the flight test show the precision of the position and velocity can be improved by about 2 times when there are none high-precision nodes in the distributed network, and the precision of the position and velocity can be improved by about 10~20 times when there are just one high-precision nodes in the distributed network.

Aiming at the shortcomings of traditional artificial potential field (APF) for solving obstacle avoidance problems, such as local minimum value and unreachable target, an optimal consensus control method taking advantage of obstacle avoidance effect and combination with APF is proposed. Based on the double integrator UAVs formation model with fixed undirected communication topology, the optimal consensus control protocol with obstacle avoidance cost function is introduced to solve the limitation of APF obstacle avoidance. At the same time, the formation control of multiple UAVs is developed to make the consensus performance index, control consumption performance index and obstacle avoidance performance index of UAVs formation control system reach the optimal solution. In addition, by establishing a virtual repulsion potential field for each UAVs, the collisions among the UAVs during the obstacle avoidance process are prevented. The simulation results show that compared with the non-optimal consistency control of the improved APF, the optimal consistency control of the improved APF proposed can shorten the task time by 32 % and can greatly maintain the integrity of the formation and reduce the consistency consumption and control loss caused by obstacle avoidance.

The frequency domain analysis of the adaptive augmentation control for a launch vehicle is focused in this paper. Firstly, the adaptive augmentation control framework is modified and considered as a single-input single-output block in order to simplify the frequency domain analysis. Furthermore, the frequency domain analysis of the controller is performed by linearizing the differential equation and using a simplified analytical solution, and then the frequency domain model is obtained. Finally, the adaptive augmentation controller is validated during the flight test on a reusable launch vehicle prototype. The result of flight data shows that the frequency model of adaptive augmentation controller is accurate and can achieve better control performance.

A dynamic load active compensation method is proposed, which is based on electromagnetic force generator to improve the operating conditions of the shaft system. Firstly, the structure and working principle of the electromagnetic force generator is introduced, and its design parameters are provided. Then, the dynamic model of CMG system is conducted. Secondly, the output characteristics of the electromagnetic force generator are calculated by using the equivalent magnetic circuit method and finite element method. The static and dynamic output characteristics of the electromagnetic force generator are tested through experiments that are compared with the calculation results, and the model parameters of the device are fitted. Finally, the dynamic load active compensation effect of the electromagnetic force generator is researched through simulation.

Two optimization-based methods for rational parameter design of adaptive augmenting control (AAC) with complex parameters are proposed in this method. The first method based on particle swarm algorithm is used to optimize parameters for minimal system impact under nominal conditions and enhance adaptability during overcoming significant disturbances. The second method is used to redefine filters, which optimizes fewer parameters with faster genetic algorithm computation. Simulation is implemented to validate optimized parameters, by showing AAC's superior disturbance rejection over PD control and smoother control commands than sliding mode control. In the second method, genetic algorithm outperforms particle swarm algorithm at parameter optimization which confirms its superiority.

A negotiated differential game optimal evasion strategy for multi pursuit-evasion is proposed for the optimal avoidance in the pursuit-evasion game in capture the flag (CTF). In order to solve this problem, an optimal evasion strategy based on a negotiated differential game is proposed. Firstly, the CTF pursuit-evasion linear model is established and the model reduced in order. Secondly, regarding the cost function based on the energy constraint and the distance between the two sides at the intersection time, Hamilton function is established. Finally, by applying the Hamilton-Jacobi-Isaacs (H-J-I) equations, the optimal evasion strategy based on the negotiated differential game is obtained. The designed strategy is simulated in “two chasing one” and multiplayer pursuit-evasion scenarios. The results show that the flag capture team has the least energy consumption while successfully capturing the flag, which verifies the effectiveness and applicability of the optimal evasion strategy based on a negotiated differential game in this paper.

Anomaly detection of spacecraft telemetry data by supervised machine learning is a challenging task due to the lack of priori knowledge. A spacecraft signal intelligent anomaly detection algorithm integrating with attention mechanism is proposed for solution. Firstly, the long-distance characteristics of spacecraft telemetry data are captured by attention mechanism, and an instruction is provided for anomaly tracing in the attention relationship matrix. Then, the stacked auto-encoder compresses the data dimension and reassembles the input signal to obtain the error reconstruction sequence. Furthermore, the anomaly indexes of the error reconstruction sequence are marked by the window threshold method to realize the anomaly detection of the spacecraft telemetry signal. Finally, the effectiveness of the proposed algorithm in terms of improving the anomaly detection performance and interpretability of spacecraft telemetry signals is verified by using a multi-channel spacecraft telemetry signal example.

In view of the characteristics of China’s space application information system and the construction of space station, a kind of planning method of scripted tasks based on time flow events is proposed for the telescience missions of space robot. Robot programming language is designed to describe the tasks of space robot, the pre-simulation is verified by tele-programming and the correct scripted task block is uploaded to the space robot controller through IP tunnel communication, and event table time-driven mechanism is used to realize autonomous tasks for on-orbit space robot. This method can reduce the real-time interaction control instructions and avoid the time delay. It meets the requirements of teleoperation of space robots when dexterous robotic arm is carrying out the task of simulation deduction in experiment cabinet of Chinese space station.