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According to the complicated condition and special dynamic performance of the Ducted Fan UAV, a nonlinear multiple input and multiple output control method is designed. This method is based on the sliding mode adaptive theory. Introducing the integral term, this method can work without the derivative value of the tracked signal; even the derivative doesnt exist or cannot be got. The adaptive control can identify the uncertain parameters and external disturbance, and adjust the controllers parameters real-time. Using a low-pass filter can effectively inhibit the chattering phenomenon in sliding mode control system. The Ducted Fan UAV attitude is controlled by this method. The simulation results show that this method has strong robustness and good tracking performance.

For UAVs are favored by many countries, the reason is that they are not affected by man-induced factor. While the ducted fan UAV is a new type of UAV, rises in recent years, but it has become very hot in the field of the research and development of miniature unmanned aerial vehicle. This paper is based on the research of a kind of small ducted fan UAVs, and works on the system parameter identification of the UAV and the design of attitude controller.

In research of SINS/GPS/SS integrated navigation system and multi sensor data fusion method, we proposed an adaptive federated Kalman filtering method based on convex optimization. The method uses real-time OLS-SVM obtained information distribution factor, so that the information distribution factor can change with the local filter performance. So that can make timely response to the performance and failure of local sensors and filter, which affects the whole system accuracy. The simulation results show that, the method has stronger adaptability of model and noise interference, can effectively restrain the divergence and improves the system precision and real time.

The INS/GPS integrated navigation as the research object, based on in-depth analysis of the multiple model filter method, the convex optimization hybrid filtering method based on adaptive optimization. OLS-SVM method utilizes real-time to obtain the weighted value of the hybrid filter, enables the weight value can be varied with the real-time filtering effect of changes. Therefore, it can effectively improve the system robustness, thus affecting the estimation precision of the whole system. The simulation results show that, the method of state model instability and filter unreliable has strong adaptability, can effectively restrain the divergence of Kalman filter, which improves the system's accuracy and robustness.

According to the situation that the statistical characteristics of noise in initial alignment of sins of UKF filter is not agree with actually, the filtering precision will severely reduce or even divergent, a combination of support vector machine method of initial alignment is proposed. In this paper, the test samples are split into four groups. Three groups are trained for the first layer and the last group is trained for the second layer of support vector machine. The first layer is a group of support vector machine in parallel computing, the second layer is an information fusion of the single support vector machine in the first layer, and combined support vector machines. In this method initial alignment of strapdown inertial navigation system is achieved. Finally through the UKF filter, SVM, CSVM simulation contrast, the results show that CSVM has an improvement than a single SVM, better real-time than UKF filter and generalization ability.

This paper deals with the aerodynamic modeling of a small ducted fan UAV and the problem of attitude stabilization when the parameter of the vehicle is varied. The main aerodynamic model of the hovering flight UAV is first presented. Then, an attitude control is designed from a linearization of the dynamic model around the hovering flight, which is based on theH∞output feedback control theory withD-stability. Simulation results show that such method has good robustness to the attitude system. They can meet the requirements of attitude control and verify further the feasibility of such a control strategy.

A novel multiagent system (MAS) has been proposed to integrate individual UAV (unmanned aerial vehicle) to form a UAV team which can accomplish complex missions with better efficiency and effect. The MAS based UAV team control is more able to conquer dynamic situations and enhance the performance of any single UAV. In this paper, the MAS proposed and established combines the reacting and thinking abilities to be an initiative and autonomous hybrid system which can solve missions involving coordinated flight and cooperative operation. The MAS uses BDI model to support its logical perception and to classify the different missions; then the missions will be allocated by utilizing auction mechanism after analyzing dynamic parameters. Prim potential algorithm, particle swarm algorithm, and reallocation mechanism are proposed to realize the rational decomposing and optimal allocation in order to reach the maximum profit. After simulation, the MAS has been proved to be able to promote the success ratio and raise the robustness, while realizing feasibility of coordinated flight and optimality of cooperative mission.

An improved support vector machine (SVM) model is proposed to perform online fault detection of the navigation system with hemispherical resonator gyro (HRG). The proposed model is based on sliding window SVM prediction and least square (LS) method, which can satisfy the prediction demand of the HRG output characteristic of nonlinearity, non-determinism and randomness. The proposed model can overcome the explosion of calculation of traditional SVM method, and it also improves the prediction accuracy compared to the GM(1,1) model and BP neural network. Finally, simulations of HRG fault patterns are used to verify the correctness and effectiveness of the online fault detection model.

Objective: To test the microRNA-181c (miR-181c) expression in tissues and plasma of gastric cancer (GC) cases, analyze any correlations, and explore the possibility of miR-181c as a potential molecular marker for GC diagnosis. Materials and Methods: Relative miR-181c expression levels in cancers and plasma from 30 GC patients was tested using reverse transcription−real-time fluorescent quantitation PCR and compared to that in samples from 30 gastric ulcer and 30 chronic gastritis patients. Results: The miR-181c expression level in the GC tissues was significantly higher than that in the gastric ulcer and chronic gastritis tissues ( P = 0.000), as was the miR-181c expression level in the GC plasma (P = 0.000). We determined that miR-181c expression in GC plasma was positively correlated to its expression in the GC tissues (P = 0.000). Conclusions: The expression of miR-181c is upregulated in GC tissues and plasma, and the miR-181c expression level in GC plasma is positively correlated to that in the corresponding cancer tissues. Plasma miR-181c is possibly a new serological marker for GC diagnosis.

A initiative and self-governing hybrid Agent system having fine capabilities of reaction and thinking has been designed to solve the problem of UAV(Unmanned Aerial Vehicle) Coordinated flight and Cooperative mission. UAV coordinated flight is realized by devising the tracking algorithm and Artificial Potential Field method in the system. For the cooperative mission in the system, the BDI model is used to model and classify the mission. Via analyzing the coupled and dynamic properties of the mission, the algorithms of auction and bidding are devised by utilizing auction mechanism improved through Prim Potential Algorithm and Particle Swarm Algorithm to complete the rational decomposing and optimal allocation of the mission then to achieve the maximum of the system profit. After calculating and simulating by the software, this system has been proved owning flexibility and stability and being able to realize the feasibility of coordinated flight and the optimality of cooperative mission, it can save human resources as well as promote the success ratio and efficiency of accomplishing the mission, at the same time, provide a great theoretical basis for the future realization of the actual UAV battle synergy.

This paper studies the robust fault-tolerant control problem of the linear uncertain system in actuator failures. Using linear matrix inequalities, a H-inf state feedback fault tolerant control method which can meet the regional pole constraints is proposed. This method has integrity to the actuator. It is applied to attitude control system of the ducted fan UAV. The simulation results shows that this method has good robustness for the ducted fan UAV's attitude control system when actuator is under the condition of performance degradation. Meanwhile closed-loop poles can be placed at a prescribed disk region.

In view of technological requirement of the long life satellite, influence of temperature variation on zero bias of HRG is investigated. By testing the temperature of HRG, it can be seen that great influence on gyro bias are caused by temperature variation, which does not satisfy the accuracy requirement. For different application fields, the time series analysis method with multiple variables and limited memory recursive least square algorithm are employed, respectively. Moreover, based on the data detected, the parameters of the constructed model are identified. Then, the timeliness and the accuracy of the two algorithms are compared. Finally, by detecting multiple sets of data, the comparison results between the two models denote that temperature drift is compensated accurately and the effectiveness of the constructed model is indicated.

In this paper, the decoupling problem for a three-axis simulator is considered. The dynamic model of the simulator is established firstly, and the reversibility of the model is proved. The three-axis simulator is highly coupled and nonlinear. The nonlinear autoregressive exogenous input (NARX) neural network is introduced to identify the inverse model of the simulator. A Lyapunov function is introduced to prove the convergence of the network. A strategy for inverse model decoupling control is proposed. The decoupling result in this paper supports the theory and decoupling strategy.