Virtual Reality-based Teleoperation with Robustness Against Modeling Errors

This article investigates virtual reality (VR)-based teleoperation with robustness against modeling errors. VR technology is an effective way to overcome the large time delay during space robot teleoperation. However, it depends highly on the accuracy of model. Model errors between the virtual and real environment exist inevitably. The existing way to deal with the problem is by means of either model matching or robot compliance control. As distinct from the existing methods, this article tries to combine model matching and robot compliance control. On one hand, the status of the virtual robot is corrected by using the position sensor data from robot joints before and during teleoperation, and the pose of the virtually manipulated object is obtained with visual recognition technology. On the other hand, compliance control algorithms of impedance control based on joint torque sensors and hybrid position/force control based on a wrist sensor have been executed in order to eliminate the small sustaining model errors. A VR-based teleoperation system of satellite on-orbit self-serving is built up. In order to verify the proposed method, an experiment deploying the solar panel troubled by malfunction is carried out through teleoperation. It shows that the large model errors are removed with the model matching method and the adopted compliance control is robust against the remaining small model errors.     

Pop-up threat models for persistent area denial

Pop-up threats usually appear or disappear randomly in a battle field. If the next pop-up threat locations could be predicted it would assist a search or attack team, such as in a persistent area denial (PAD) mission, in getting a team of unmanned air vehicles (UAVs) to the threats sooner. We present a Markov model for predicting pop-up ground threats in military operations. We first introduce a general Markov chain of order n to capture the dependence of the appearance of pop-up threats at previous locations of the pop-up threats over time. We then present an adaptive approach to estimate the stationary transition probabilities of the nth order Markov models. To choose the order of the Markov chain model for a specific application, we suggest using hypothesis tests from statistical inference on historical data of pop-up threat locations. Anticipating intelligent responses from an adversary, which might change its pop-up threat deployment strategy upon observing UAV movements, we present adaptive Markov chain models using a moving horizon approach to estimate possibly abrupt changes in transition probabilities. We combine predicted and actual pop-up target locations to develop efficient cooperative strategies for networked UAVs. A theoretical analysis and simulation results are presented to evaluate the Markov model used for predicting pop-up threats. These results demonstrate the effectiveness of cooperative strategies using the combined information of threats and predicted threats in improving overall mission performance.     

DBV-RCS中一种提高时延性能的时隙分配方法(英文)

在DVB-RCS卫星通信中,为某个业务分配的时隙在MF-TDMA帧中的位置分布将影响该业务的时延特性。本文分别针对固定速率和实时可变速率业务,对MF-TDMA的帧长度、终端分配的时隙数量及位置分布与业务时延性能之间的关系进行了建模分析。理论分析表明时隙位置是影响业务时延的一个重要因素,位置分布越均匀,越能提高业务的时延性能;时隙均匀分配相比传统的时隙连续分配可以有效降低业务平均时延,且时延不随帧长的变化而变化;在时隙均匀分配方式下,提高实时可变速率业务的带宽分配可以更有效的提高时延性能。在DVB-RCS标准基础上,提出了一种与标准完全兼容的时隙均匀分配方法(TUAM),通过计算机仿真验证了算法的有效性以及建模分析的正确性,该算法可有效地保证业务具有极低的平均时延和时延抖动,使得VoIP等实时业务不会由于端到端时延过大而丢包,提高了DVB-RCS系统对于实时业务QoS的保障能力。     

从计划到规划：宏观经济管理手段的逻辑建构

通过对计划和规划概念上的比较分析,对市场经济条件下规划存在的必要性进行了探讨,并给予了肯定回答.在市场经济条件下规划具有经济宪章、促进经济社会协调发展、遏制政府失灵、构建经济伦理四个方面的作用,从而为规划作为宏观管理的重要手段建立了一个有解释力的逻辑框架.     

读书的经济学分析与对策

文章分析了当前读书无用论的成因,包括学费过高、就业形势不好、工作待遇不高等现象,指出教学实用性不强是形成这一现象的原因,并且提出了一些解决措施,如加大科研投入和提高对研究开发的重视程度,让知识分子有用武之地,提高教学质量,加大教育投入,通过社会保障来调节工作待遇.     

Minimum sliding mode error feedback control for fault tolerant small satellite attitude control

This paper proposes a new control strategy (which we call “minimum sliding mode error feedback control, MSMEFC”) for small satellite attitude control. As we know, the attitude control algorithm plays a significant role in the whole performance of the satellite, especially under the existence of uncertain disturbances from the space. Without loss of generality, the MSMEFC is presented based on the sliding mode theory. It is assumed that the equivalent control error is defined to offset the uncertain disturbances to improve the control performance. Hence, in order to estimate the optimal equivalent control error, a cost function is derived on the basis of the principle of minimum sliding mode error. Then, the equivalent control error wills feedback to the conventional sliding mode control to obtain the final MSMEFC. According to the theoretical analyzes, the sliding mode after the MSMEFC will approximate to the ideal sliding mode, resulting in enhancing the control performance. Moreover, an adaptive non-singular terminal sliding mode is employed to compare with the performance of MSMEFC. Several simulations are performed to verify the effectiveness of proposed MSMEFC in the presence of serious perturbations, even in some fault-tolerant scenarios.     

Approach to Interference Aircraft Automatic Riveting Process Control of Drilling and Riveting

Interference fit riveting is an effective way to improve the fatigue life of aircraft. The accurate control of riveting interference of aircraft automatic drilling and riveting equipment is achieved by process parameters including upsetting force and upset head height. It is valuable for aircraft manufacturing engineering. An approach to interference riveting process control based on the analysis of interference riveting stress field is proposed. According to assembly structure, the upsetting force is calculated by the material property and interference fit level, and the upset head height is deduced by the upsetting force. The experimental result shows that the interference fit level can be controlled accurately by the upsetting force and upset head height, and then, the quality of aircraft auto- matic riveting can be improved. The proposed approach is verified by the good match between the predicted result and the experimental result.     

Leading-edge flow separation control over an airfoil using a symmetrical dielectric barrier discharge plasma actuator

In order to promote an in-depth understanding of the mechanism of leading-edge flow separation control over an airfoil using a symmetrical Dielectric Barrier Discharge(DBD) plasma actuator excited by a steady-mode excitation, an experimental investigation of an SC(2)-0714 supercritical airfoil with a symmetrical DBD plasma actuator was performed in a closed chamber and a low-speed wind tunnel. The plasma actuator was mounted at the leading edge of the airfoil.Time-resolved Particle Image Velocimetry(PIV) results of the near-wall region in quiescent air suggested that the symmetrical DBD plasma actuator could induce some coherent structures in the separated shear layer, and these structures were linked to a dominant frequency of f0= 39 Hz when the peak-to-peak voltage of the plasma actuator was 9.8 kV. In addition, an analysis of flow structures without and with plasma actuation around the upper side of the airfoil at an angle of attack of18° for a wind speed of 3 m/s(Reynolds number Re = 20000) indicated that the dynamic process of leading-edge flow separation control over an airfoil could be divided into three stages. Initially, this plasma actuator could reinforce the shedding vortices in the separated shear layer. Then, these vortical structures could deflect the separated flow towards the wall by promoting the mixing between the outside flow with a high kinetic energy and the flow near the surface. After that, the plasma actuator induced a series of rolling vortices in the vicinity of the suction side of the airfoil, and these vortical structures could transfer momentum from the leading edge of the airfoil to the separated region, resulting in a reattachment of the separated flow around the airfoil.     

Adaptive neural network control of nonlinear systems with unknown dynamics

In this study, an adaptive neural network control approach is proposed to achieve accurate and robust control of nonlinear systems with unknown dynamics, wherein the neural network is innovatively used to learn the inverse problem of system dynamics with guaranteed convergence. This study focuses on the following three contributions. First, the considered system is transformed into a multi-integrator system using an input–output linearization technique, and an extended state observation technique is used to identify the transformed states. Second, an iterative control learning algorithm is proposed to achieve the neural network training, and stability analysis is given to prove that the network’s predictions converge to ideal control inputs with guaranteed convergence. Third, an adaptive neural network controller is developed by combining the trained network and a proportional-integral controller, and the long-standing challenge of model-based methods for control determination of unknown dynamics is resolved. Simulation results of a virtual control mission and an aerospace altitude tracking mission are provided to substantiate the effectiveness of the proposed techniques and illustrate the adaptability and robustness of the proposed controller.