The boundary estimation algorithm of fault-tolerance performance was researched.By introducing the characteristic parameters of control surface dilapidation,the relationship between the control surface dilapidation and the characteristic polynomial of the control system was established.Based on the UAV flight quality,the boundary estimation problem of fault-tolerance performance was converted into a generalized stability problem of interval polynomials.Then,one class of stability problem of generalized stable region was solved by using Soh polynomial lemma.And the other class is transformed into a stability problem of interval polynomials,which can be analyzed by the Hurwitz stability criterion.Finally,a specific example was adopted to prove the feasibility of the method.

Multiple Unmanned Aerial Vehicle (UAV) system self-organization cooperative operation based on consensus theory has become an effective operational mode for dealing with sudden tasks,mission rendezvous is the first action and vital part of self-organized cooperative control.After giving the problem description of self-organization cooperative and optimal control under the cooperative game framework,and establishing Pareto Solution Set of optimal control input,the basic cooperative game optimal consensus control algorithm is presented by Nash Bargaining Solution under self-organization cooperative control structure based on fast consensus control algorithm,which can achieve optimal control objectives such as optimal energy cost in the coordination process of system task status,dynamic response of cooperative control,timeliness of rendezvous action.After introducing outdated status difference into the basic algorithm,and optimizing the weighted matrix of the cost function by genetic algorithm,which is based on the fitness function constructed of optimal control targets,the improved cooperative game optimal consensus control algorithm is presented.Theoretical analysis and simulation results verify the feasibility and effectiveness of the method.

Considering the uncertain factors as modeling errors and external disturbances existed in nonlinear systems,a Nonlinear Dynamic Inversion (NDI) control method was proposed based on Sliding Mode Disturbance Observer (SMDO) on-line compensation.The SMDO was designed for estimating the uncertain factors,and the output of it was used to design a new compensating control law,which was used together with NDI control for eliminating the influence of the uncertain factors.The simulation experiment based on Herbst maneuver is given to show the difference between the simplex NDI control and the proposed method.The simulation results show the effectiveness and robustness of the proposed method.

A visible light image simulation method for space-based observation of space target is presented.The speed and position information of the observation platform and the target is generated by the STK,and the imaging process is implemented by Matlab programming.The superiority of this method is to use the visual function of STK,and the orbital parameters of the observation platform and target can be controlled to make the target enter the FOV of camera.Matlab has a powerful image processing library,which can add all kinds of noises and effects to the image during sequence image generation to make the image more realistic.The generated simulation images are mainly used for research on space target detection algorithm.Finally,the motion properties of space target and the stars in simulation image are analyzed,which provides a basis for improving the performance of space target detection algorithm.

In actual target tracking process,the state and the type of target being tracked are uncertain.By using the state information and the feature information obtained respectively by kinematic sensor and attribute sensor,we obtained a joint state/type probabilistic density function for describing the uncertainty of target state and target type,and proved that the system model under the linear Gaussian assumptions is a Gaussian mixture model.Based on this property,a Gaussian mixture filter was introduced to implement maneuvering target tracking effectively.In simulation and analysis,the effectiveness of the tracking algorithm using Gaussian mixture filter was validated by comparing it with two other classic tracking algorithms.

Aiming at the problem that the matching function of sensor to target is difficult to determine in multi-sensor,multi-target association,a sensor assignment method was given.It was proposed that in the scenario of the actual early warning detection combat,the matching ability of sensor to target was decided jointly by collaborative coefficient between the sensors,information value function and information cost function.Through the analysis and comparison of the sensor detection ability and existing target information,the matching value of sensor to target was calculated out.Then,taking the maximum comprehensive efficiency of the sensor-target association as the objective function,the assignment model applicable to early warning detection was established.Finally,the simulation result verified the rationality and effectiveness of the proposed method,and showed that the association scheme is simple and feasible.

The problem of Unmanned Aerial Vehicles (UAVs) cooperative searching in the uncertainty environment was studied.In order to realize target optimization,the models of uncertainty environment,the UAVs,network communication relationship and cooperative search/detect updating were established.A cooperative searching control method based on Uncertainty Value Reducing Maximization(UVRM) was presented.The problem of UAV cooperative searching in the uncertainty environment was solved by this control arithmetic,and the mathematic characteristic was proved.Simulation was made for control arithmetic of cooperative searching from different aspects.The simulation results show that the algorithm can not only implement cooperative search,but also ensure spreadability of the searching area and superiority of the searching time.

The multi-sensor track association under the influence of systematic bias is discussed.The algorithms that utilize target topology information are not reliable for track association due to the false alarm and missing detection in sensor observation.Thus we proposed an anti-bias track association algorithm based on target course reference.Firstly,a moving coordinate system was established taking the position of the observed target as the origin and its course as reference direction,and then rotation/translation transformation was made to the neighboring targets' data.Secondly,the distance of neighboring targets in different target reference coordinate system was used as the statistics for global optimal track association.The average distance of confirmed associated pairs in all target reference coordinate systems was taken as the matching metric,and the corresponding reference target pair reaching the minimum matching measurement was regarded as matched targets.The simulation results prove the effectiveness and robustness of the algorithm from several aspects including the target common observation ratio,systematic bias range and target density.

The sliding mode guidance law of the reentry phase of vehicle was studied considering terminal velocity and impact angle constraints.The sliding mode guidance law satisfying terminal angle was derived,the guidance equations were expressed in longitudinal and lateral plane respectively,and the needed guidance command of angle of attack and sideslip angle were generated.Guidance command of angle of attack and sideslip angle were obtained considering the terminal velocity and falling angle.The synthesized guidance command was the sum of needed guidance command and appended guidance command.Simulation results indicate that the attitude of vehicle is stable and the vehicle is guided to target precisely.The changes of impact point deviation,terminal velocity and impact angle with target velocity were analyzed.The result shows that:the sliding mode guidance law and the optimal guidance law have similar performance on impact angle control,but the former performs much better on terminal velocity and impact point deviation.Therefore,the sliding mode guidance law is robust to maneuvering target.

Since the traditional threat assessment methods can’t reflect the changing of threat factors during air combat,a dynamic weight calculating method was proposed based on particle swarm and radical basis function neural network (PSO-RBF) by introducing RBF neural network and using a new structure entropy weight method to optimize the training parameters of RBF neural network.On the circumstance of assessing the threat degree during multi-UCAV cooperative combat,simulations were executed by using structure entropy weight method and PSO-RBF method respectively.The result proved that the PSO-RBF process can assess the threat degree of the target during air combat effectively and make the strategic decision more objective and reasonable.

Attack decision-making is very important for UAVs in air-to-ground attacking,which consists of weapon-target assignment (including weapon selection and weapon allocation) and target decision-making (including target selecting and target sequencing).Based on the information of targets,carrier-aircraft,and weapons,the types of weapon were selected through Dynamic Bayesian Networks (DBN) and it's inference model.Then,weapon assignment was implemented based on the Monte Carlo simulation results.According to the results obtained through the steps before,target selecting and target sequencing were accomplished.Simulation was made and the result verified the rationality and validity of the decision-making method for multi-target attacking.

The landing precision of an Unmanned Aerial Vehicles (UAVs) is influenced seriously by the complexity of the environment.In this article,we focus on the accuracy landing of UAV,and analyze the main factors that bring in errors.Some methods were given for improving the performance and environmental adaptability of UAVs in landing process.Simulation was made with Monte Carlo method to examine the suitability of these control methods under different sort of uncertainties.The results show that the improved control method can reduce the deviation of landing points,and increase the landing precision.

The large transport aircraft has high requirement to the safety and comfort level of passengers,and automatic brake control system has become an urgent needed system.An automatic brake control system applicable for large transport aircrafts is presented in the paper.The scheme and architecture of the system are given,and analysis is made to the working principle of the system.The system control logic is put forward,and the control law based on the constant reduction rate is studied.Computer simulation was carried out.A dynamic system test was made to the automatic brake control system on an inertial test bench,and the test results achieved the expected objectives.

A power management system was designed according to the requirement of the integrated,modular aircraft core processor.Based on parallel power supply mode,the distributed power architecture is adopted in the system.The power transformation unit,switch array unit,intelligent control unit are designed,and intelligent management software is integrated with communication protocol.Over current protection, independent electric control,current/voltage and temperature acquisition,interactive communication control and current sharing techniques are implemented,and intelligent management,state monitoring,fault recording,and redundancy and load sharing are realized,which can improve digital and intelligent level of the power management system.The system is safe,reliable and stable,and has been verified by engineering application.

In view of the multiple structure failure modes appeared previously in experiments,we added a structure reliability prediction under multi-failure mode based on the traditional electronics prediction when implementing system reliability prediction to a new type actuator system.Firstly,the Expert Grading Allocation Method was used to allocate system reliability index to the corresponding modules and components.Then,the reliability index of electronic components was obtained by Component Counting Method,and the structure reliability index under each failure mode was obtained by FOSM method.Thus the system reliability index was obtained.The prediction results show that:1) The reliability indexes of design scheme achieve the requirement of reliability index allocated for the missile;and 2) The reliability index of ball bearing screw under multi-failure mode is lower than that of the other electronics modules,which is consistent with the previous failure phenomenon in experiments.

According to problem of high false alarm rate in ship detection produced by clouds in optical remote sensing image,a detection algorithm based on serial-parallel and multi-feature fusion is proposed.First,the segmentation of ships and clouds is realized by Gaussian-gray-threshold,and the regions of binary image are marked.Then,the serial-parallel and multi-feature fusion algorithm is constructed by the geometric morphological and edge-energy characteristics,including center deviation degrees,length-width ratio,area changing ratio,sinuosity and degree of filling.False alarms caused by clouds are effectively eliminated though the comprehensive judgment of region of interesting (ROI),thus the problem of high false alarming rate is solved.The effectiveness of the algorithm is verified by the experiment results with multiple optical remote sensing satellite images of SPOT4.