This paper proposes a tightly coupled visual-IMU odometry. Before fusing visual and inertial measurements, a real time and robust image matching algorithm which is based on inertial information is proposed. Delaunay triangulation and three consecutive image matching method are employed to remove sporadic outliers. Also, non-maximum-suppression and inertial information are used to decrease feature searching time. Consequently, a tightly coupled UKF fusion method is used to estimate ego-motion. The trifocal tensor geometry relationship between three consecutive images is used as measurement information which is wrapped into a RANSAC scheme to be robust against outliers. Therefore, the proposed method can handle well in dynamic environment. The experiments show the effectiveness of the proposed method in KITTI data which is a publicly available real world dataset.

In this paper, the Linear Parameter Varying (LPV) model and Model Predictive Control (MPC) method are proposed and applied on a morphing wing UAV (MWUAV) for its transient mode. In the model method, an improved function substitution method is introduced, the proposed method combines function substitution and partial linearization, using which the derived LPV model is not necessarily polytope and is more consistent with the original nonlinear model. Then, an MPC controller is derived based on the LPV model. Because the LPV model is polytope, parameter dependent receding-horizon optimization process is introduced and solved with parameter dependent quadratic programming. After that, a comparative simulation with gain-scheduled method based on NSGA-II is performed, the simulation results show that the response of MPC controller can track the control command more accurately with more effective control effort.

This paper focuses on two challenges for temporal action localization community, i.e., lack of long-term relationship and action pattern uncertainty. The former prevents the cooperation among multiple action instances within a video, while the latter may cause incomplete localizations or false positives. The lack of long-term relationship challenge results from the limited receptive field. Instead of stacking multiple layers or using large convolution kernels, we propose the intra-video attention mechanism to bring global receptive field to each temporal point. As for the action pattern uncertainty challenge, although it is hard to precisely depict the desired action pattern, paired videos that share the same action category can provide complementary information about action pattern. Consequently, we propose an inter-video attention mechanism to assist learning accurate action patterns. Based on the intra-video attention and inter-video attention, we propose a unified framework, namely I2Net, to tackle the challenging temporal action localization task. Given two videos containing sharing action categories, I2Net adopts the widely used one-stage action localization paradigm to dispose of them in parallel. As for two neighboring layers within the same video, the intra-video attention brings global information to each temporal point and helps to learn representative features. As for two parallel layers between two videos, the inter-video attention introduces complementary information to each video and helps to learn accurate action patterns. With the cooperation of intra-video and inter-video attention mechanisms, I2Net shows obvious performance gains over the baseline and builds new state-of-the-art on two widely-used benchmarks, i.e., THUMOS14 and ActivityNet v1.3.

In control practices, problems of parametric or time-varying uncertainties must be dealt with. Robust control based on norm theory and convex and non-convex optimization algorithms is a powerful tool to solve these problems in theory, but it is employed rarely in applications. In most engineering cases, Proportional-Integration-Derivative (PID) control is still the most popular method for its easy-to-tune and controllable properties. The control method proposed in this paper integrates the PID control into robust control formulation as a robust Structured Static Output Feedback (SSOF) problem of Linear-Parameter-Varying (LPV) systems, which can be converted into a Parameter Dependent Bilinear-Matrix-Inequality (PDBMI) optimization problem. A convex-concave decomposition based method is given to solve the proposed PDBMI problem. The proposed solution has a simple structure in PID form and can guarantee stability and robustness of the system being controlled in the whole operation range with less conservativeness than existing solution.

The terminal area energy management (TAEM) phase of the reusable launch vehicle (RLV) determines whether it can reach the landing window safely and accurately. Compared with offline trajectory planning, online trajectory planning is more robust to the uncertainties of actual flight. This paper investigates the online trajectory planning algorithm of TAEM phase. Firstly, the trajectory profile design method based on fixed range-to-go is proposed, and the non-linear least squares optimization algorithm is used to automatically design a trajectory profile that satisfies the fixed range-to-go. Secondly, in order to adapt to high-speed and high-sinking flight, according to the approximate linear characteristics of the dynamic pressure transition point with the fixed range-to-go, a dual optimization algorithm is used to quickly find the dynamic pressure transition point with the shortest time and satisfying the range-to-go. By establishing the trim data table, the planning time is less than 1s, which fully meets the real-time requirements of the online planning. The simulation results show that the online planning algorithm is simple, efficient and robust.

In this paper, a linear parameter-varying (LPV)-based model and robust gain-scheduled structural proportion integral and derivative (PID) control design solution are proposed and applied on a bio-inspired morphing wing unmanned aerial vehicle (UAV) for the morphing process. In the LPV model method, the authors propose an improved modeling method for LPV systems. The method combines partial linearization and function substitution. Using the proposed method, we can choose the varying parameters simply, thus creating a model that is more flexible and applicable. Then, a robust gain-scheduled structural PID control design method is given by introducing a structural matrix to design a structural PID controller, which is more consistent with the structure of the PID controller used in practice and has a simpler structure than representative ones in the existing literature. The simulation results show that the developed LPV morphing UAV model is able to catch the response of the original nonlinear model with a smaller error than the existing Jacobian linearization method and the designed controller can maintain stable flights in practice with satisfactory robustness and performance.

天线测试对于天线辐射性能的检验至关重要.而当天线的尺寸较大或测试频率较低时,传统的天线测试方法难以满足测试需求.以大型天线测试为研究背景,研究了一种基于小型无人机的天线测试方法,根据测试信道模型获取被测天线的增益方向图.与传统的利用GPS作为无人机的导航信号相比,采用实时差分GPS作为无人机的导航信号,结合几何控制方法精确控制无人机的飞行轨迹,无人机的飞行轨迹偏差小于0.1m.对测试方法进行了试验验证,结果表明采用该方法可以较为准确地测量天线的方向图,增益测试误差小于1 dB,具有较高的工程应用价值.

针对飞机飞控系统仿真建模研究与开发,提出了一种可自动升阶的仿真建模方法.上述方法是基于Matlab的ModelVariants技术实现的,有效地解决了在飞控系统仿真建模研究设计开发过程中,在不同的设计研究阶段需要使用不同的仿真模型来达到不同的仿真目标的问题.比起传统的不断大量修改替换模型的方法,所提方法无需修改模型,只需简单更改初始化脚本文件即可实现,大大地提高了开发效率、标准化程度以及可靠性,同时为飞控系统仿真建模设计开发的平台化提供了技术方案.

In order to improve the picking speed and accuracy of robot, the objects detection and localization algorithm based on Mask RCNN and stereo vision is designed to complete the autonomous detection and 3D spatial location of the target to be detected. Aiming at the problem that the detection accuracy of the neural network may be low and the object contour centroid estimation is not accurate, the ORB descriptor is used to confirm the target contour matching centroid. The experimental results show that the proposed algorithm can accurately accomplish the object detection and localization, and it is of great significance for the research of fully automatic picking robots.

Research on the method of measuring the square wave voltage of oscilloscope calibrator based on digital voltmeter

Further research based on the author's past work in [1] is performed in this paper. A structural robust gain-scheduled (GS) PID control method is proposed and applied to control the pitch angle for a morphing wing UAV(MUAV) in its transient process. Firstly, the author gives the robust static output feedback (SOF) control method for LPV system and shows that PID control is equivalent to SOF control, thus the robust GS PID control design is derived via SOF. Secondly, the author introduces a structural matrix to structuralize the robust GS PID control, then a structural robust GS PI(D) control design method is derived. The controller is in the same form of PID controller used in practice, which has a more simple structure than the result in [10] and other existing results. At last, the proposed method is applied to control the pitch angle of a MUAV based on a LPV model developed by a Jacobian Linearization method. Linear analysis and nonlinear Monte-Carlo simulation were performed and the results showed that the proposed solution were applicable with satisfactory robustness and performance.

The classic Mean-Shift algorithm lacks the necessary template update, because window size remains the same in tracking process, tracking will fail when the template scale change, track will be ineffective when the template is faster, the feature of histogram seems simple in the object color characteristic described aspects and lacks space information. This paper presents a Cam-Shift clustering algorithm, regarding the centroid position of the moving object which is detected as the first iteration of the input frame, narrowing the visual search range, shortening the matching time. Through the template update of color model and cluster, the algorithm resists the interference of light, strain, shelter and achieving more precise moving object motion parameter estimation and tracking results than the existing algorithms to a certain extent. Experiment is used to verify each algorithm in this article. Selecting aerial image sequence multiple moving vehicle object to study, results are capable of getting sustained and effective track to multiple moving objects in image sequences.

This paper proposes a tightly coupled visual-IMU odometry. Before fusing visual and inertial measurements, a real time and robust image matching algorithm which is based on inertial information is proposed. Delaunay triangulation and three consecutive image matching method are employed to remove sporadic outliers. Also, non-maximum-suppression and inertial information are used to decrease feature searching time. Consequently, a tightly coupled UKF fusion method is used to estimate ego-motion. The trifocal tensor geometry relationship between three consecutive images is used as measurement information which is wrapped into a RANSAC scheme to be robust against outliers. Therefore, the proposed method handles well in dynamic environment. In the experiments phase the KITTI data is used which is a publicly available real world dataset and has rich dynamic information. Visual odometry experiments indicate that the proposed method gives a better result between pure IMU reckoning and image motion estimation. Although the algorithm shows the effectiveness in KITTI data, it has outliers in feature matching which will give a false motion evaluation result. Future work will focus on improving the confidence level of feature matching.

Image matching is the core of the computer vision. The pyramidal image matching algorithm based on inertial in-formation is proposed to solve the problems of long computation time and sparse matching points,which exist in the current algo-rithms. Two-level image pyramid is used and the non-maximum suppression method is employed to control the number of feature points in this algorithm. When matching the four images,the algorithm makes use of the epipolar constraint and inertial informa-tion to constraint the searching space,and compares the sum of absolute differences(SAD). The simulation experimental results show that the algorithm can control the obtained matching points,and improve the real-time performance and the degree of confi-dence of the matching points.

Model and simulation problems of civil aircraft taxing are researched in this paper. Normal and Faulty models of landing gears were studied mainly, and a solution for speed oscillation was proposed. After that, the system model was setup based on Simulink. The simulation results showed that the proposed model and solution can simulate the movement of civil aircraft taxing correctly, based on which control design and simulation of ground operation can be performed.

All the present research of UAV flying qualities has been done according to the human flying quality criterion ,because there is no special UAV flying quality criterion .This paper validates the applica-bility of human flying quality criterion such as CAP and bandwidth criterions for small UAVs through ana-lyzing the UAV performance before and after the inclusion of control augmentation system ,and proposes a means to evaluate the longitudinal flying quality research of small UAV .The study of this paper can pro-vide a reference for the future research of UAV flying quality .

Model establishment of the electromechanical actuator is essentially based on the theory of mathematical derivation at present ,however ,the actuator model built by this method is not closed to the actual model.To solve this problem,the paper proposed a method to establish the model of the actuator based on the tested data ,and set up an experimental platform for data automatic collecting ,and also inno-vatively use combination window method to convert the data in the frequency domain ,get the frequency response function then use the nonlinear least squares method to identify the model of the actuator .By combination of the simulated results with the tested data ,it is shown that,in the frequency domain,the maximal absolute error of magnitude is less than 1.9 dB,in the time-domain,the error of angle is less than 1.5°,and the error of dely time is less than 10 ms,The result showed that the simulation model built by tested data is closed to the actuator .Therefore,this method can be applied well to model identification in the project .

Managing the energy of reusable launch vehicle(RLV) to make it arrive the auto-landing interface and ensure that RLV can land safely, it's the main purpose of terminal area energy management(TAEM). The emphases are trajectory programming and tracking that can meet the physical?and auto-landing interface constraints. In this article, trajectory programming is transformed into an optimized problem.Then the trajectory tracking method is built. At last, the trajectory tracking law is designed. The simulations show the method mentioned above can give a reasonable?trajectory with a high tracking precision.

Robust Gain-scheduling control of LPV (Linear Parameter-Varying) system is studied and applied on a Morphing-Wing UAV (MUAV). LPV output feedback control and issues on its realizability are researched in theory, after that, LPV model of MUAV is setup based on Jacobian linearization and an attitude control is designed in pitch axis for the whole morphing process. Structure singular value analysis and Monte-Carlo Simulation are performed to evaluate our design in linear and nonlinear cases respectively, which demonstrate satisfactory tracking performance and robustness of the controller proposed, thus showing our design is indeed effective. At last, we give a conclusion with discussion, which points out the method can be improved in two ways.

Model-Based Design is an efficient approach for complex embedded control systems,which provides a general design and test platform.Verification and test play a pivot role in system design process and keeping the quality of products.In accordance with the application of the Model-Based Design and Verification and test in the design of practical system,a detailed analysis on each step during the model-based test and validation is presented,including static test,dynamic test,and the test in host and target environment.Finally,the test and validation for a flight control software is presented in order to prove that the system design meets the requirements.