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.     

Prediction of nonlinear pilot-induced oscillation using an intelligent human pilot model

This paper proposes a method to predict nonlinear Pilot-Induced Oscillation (PIO) using an intelligent human pilot model. This method is based on a scalogram-based PIO metric, which uses wavelet transforms to analyze the nonlinear characteristics of a time-varying system. The intelligent human pilot model includes three modules: perception module, decision and adaptive module, and execution module. Intelligent and adaptive features, including a neural network receptor, fuzzy decision and adaptation, are also introduced into the human pilot model to describe the behavior of the human pilot accommodating the nonlinear events. Furthermore, an algorithm is proposed to describe the procedure of the PIO prediction method with nonlinear evaluation cases. The prediction results obtained by numerical simulation are compared with the assessments of flight test data to validate the utility of the method. The flight test data were generated in the evaluation of the Smart-Cue/Smart-Gain, which is capable of reducing the PIO tendencies considerably. The results show that the method can be applied to predict the nonlinear PIO events by human pilot model simulation.     

Boundary condition modelling and identification for cantilever-like structures using natural frequencies

The actual boundary conditions of cantilever-like structures might be non-ideally clamped in engineering practice, and they can also vary with time due to damage or aging. Precise modelling of boundary conditions, in which both the boundary stiffness and the boundary mass should be modelled correctly, might be one of the most significant aspects in dynamic analysis and testing for such structures. However, only the boundary stiffness was considered in the most existing methods. In this paper, a boundary condition modelling and identification method for cantilever-like structures is proposed to precisely model both the boundary stiffness and the boundary mass using sensitivity analysis of natural frequencies. The boundary conditions of a cantilever-like structure can be parameterized by constant mass, constant rotational inertia,constant translational stiffness, and constant rotational stiffness. The relationship between natural frequencies and boundary parameters is deduced according to the vibration equation for the lateral vibration of a non-uniform beam. Then, an iterative identification formulation is established using the sensitivity analysis of natural frequencies with respect to the boundary parameters. The regularization technique is also used to solve the potential ill-posed problem in the identification procedure.Numerical simulations and experiments are performed to validate the feasibility and accuracy of the proposed method. Results show that the proposed method can be utilized to precisely model the boundary parameters of a cantilever-like structure.     

基于ANCF的松弛绳索动力学建模与仿真

传统绝对节点坐标方法(ANCF)绳索模型是基于梁单元建立的,其不能反映纤维绳索的不可抗压和松弛特性。考虑纤维绳索初始松弛余量,给出了绳索非线性轴向应力-应变关系。绳索在松弛状态下轴向力接近于零,在张紧状态下表现出线弹性特性。在此基础上,采用绝对节点坐标方法推导了松弛绳索动力学模型。通过静力学和动力学仿真,将传统绳索模型和松弛绳索模型进行对比,结果表明:在重力及不同集中载荷作用下,松弛绳索相比于传统绳索都具有更大的变形;分析移除集中载荷后绳索的动力学响应可知,传统绳索在振动过程中始终处于张紧状态,且绳索上各点振动同步,而松弛绳索会在张紧和松弛状态之间不断转换,绳索上各点的振动存在相位差,能够更好地反映绳索在松弛状态下的动力学特性。     

中介机构参与预算绩效评价问题研究——基于财政部门的问卷调查

推进中介机构参与预算绩效评价是我国预算绩效管理的重点内容,基于财政部门的问卷调查表明：该项工作尚处于起步阶段,需要财政部门、中介行业组织和中介机构等方面协同配合,通过完善评价规范,合理设计委托代理与付费机制,加强中介机构能力培育与资格管理,落实结果应用等促进中介机构参与预算绩效评价.     

水下搅拌摩擦加工对AZ91镁合金组织和力学性能的影响

在空气中和水下对AZ91镁合金铸造板材进行搅拌摩擦加工,并对材料的微观组织和力学性能进行了分析测试。研究结果表明:由于水的强制冷却作用,水下搅拌摩擦加工板的搅拌区面积小于空气中加工板,两种板材搅拌区的微观组织均得到了显著细化,平均晶粒尺寸分别为1.9μm和3.5μm;热机影响区均形成了纤维状组织,且水下搅拌摩擦加工板的组织更为细小;空气中加工板的热影响区出现了组织粗化,而水下加工板的热影响区的晶粒尺寸与母材相当。两种加工工艺均使第二相β-Mg17Al12由原始铸态组织中连续网状变为颗粒状。由于水下加工板的组织更为细小,其硬度、抗拉强度和伸长率较空气中搅拌摩擦加工板高。     

航空工业企业架构构建方法研究

重点介绍了航空工业企业架构研究的方法体系及研究内容,并重点描述了航空工业业务架构的构建方法,用以提炼航空工业先进业务运作流程、手段与方法,确定了研究推进路线,并进行了成果验证,研究成果对推进航空工业通用业务模式的总结、表达及信息化通用参考模型的设计,信息系统的开发、实施具有指导意义。     

The Magnetospheric Multiscale Magnetometers

The success of the Magnetospheric Multiscale mission depends on the accurate measurement of the magnetic field on all four spacecraft. To ensure this success, two independently designed and built fluxgate magnetometers were developed, avoiding single-point failures. The magnetometers were dubbed the digital fluxgate (DFG), which uses an ASIC implementation and was supplied by the Space Research Institute of the Austrian Academy of Sciences and the analogue magnetometer (AFG) with a more traditional circuit board design supplied by the University of California, Los Angeles. A stringent magnetic cleanliness program was executed under the supervision of the Johns Hopkins University’s Applied Physics Laboratory. To achieve mission objectives, the calibration determined on the ground will be refined in space to ensure all eight magnetometers are precisely inter-calibrated. Near real-time data plays a key role in the transmission of high-resolution observations stored on board so rapid processing of the low-resolution data is required. This article describes these instruments, the magnetic cleanliness program, and the instrument pre-launch calibrations, the planned in-flight calibration program, and the information flow that provides the data on the rapid time scale needed for mission success.     

基于仿真分析的压铆力预测模型

铆接是飞机结构机械连接的主要方式,压铆力作为影响铆接质量的重要因素,传统的计算方法通常是建立在体积不变的假设之上,且不考虑钉杆材料被压入钉孔部分的体积,导致模型误差较大。为此,依据仿真分析结果和铆钉材料的流动趋势,引入体积缩减系数来描述钉杆被压入钉孔部分的体积,建立压铆力预测模型,并与已有的试验数据进行对比,结果表明计算值与试验值的一致性较好,该模型可以用来预测铆接过程中压铆力的大小。     

一种PowerPC和FPGA结构的远程接口单元设计方法

远程接口单元（ RIU）已经是综合化模块化航空电子系统的重要组成部分。分析了分布式IMA架构下航空电子系统对远程接口的新需求,提出了一种基于PowerPC ＋FPGA结构的远程接口单元设计方法。通过设计方法不仅实现了单元综合多种I／O资源,还保证了单元具有一定的容错和可配置特性,并在远程接口关键技术方面取得了实质性的突破。