微结构阵列超精密车床快刀伺服装置及试验研究

各种微结构阵列在LED照明、液晶显示、光学仪器等领域得到了广泛的应用,但其结构细微复杂且形状表面质量要求高,其加工工艺技术研究引起了广泛的关注。本文提出压电陶瓷驱动结合柔性铰链作为刀架的快刀伺服装置,建立快刀伺服系统的机电耦合模型,研究各系统参数对系统性能的影响规律,基于有限元仿真对铰链结构参数进行优化;通过激光位移传感器测试刀具输出位移的线性度;利用研制的快刀系统和超精密车床在红铜材质的圆柱面上进行正弦网格微结构加工,加工结果表明所设计的快刀系统能够准确有效地加工出结构复杂的微结构阵列。     

面向对象的燃气发生器地面试验柔性控制模型与实现

根据燃气发生器地面试验对控制的需求,分析了其控制行为,提出了面向对象的燃气发生器地面试验柔性控制模型。模型先进合理,符合目前测控技术发展的方向。在该模型基础上实现了柔性控制系统,能够满足各种型号的燃气发生器地面试验对控制的需求,并在试验中得到了成功的应用。与以前的控制系统相比,该系统具有可靠性高、结构简单、操作简单灵活等特点。     

传感器组网的关键技术

传感器组网是协同作战的关键技术,从作战的动态过程角度看,传感器组网是以特定战术任务为目标,在通信技术和计算机技术支持下的战术决策。从完成特定战术任务的能力角度看,传感器管理技术、通信技术、信息融合技术以及计算机技术是影响传感器网络性能的关键因素。     

Resale price maintenance contracts with retailer sales effort: Effect of flexibility and competition

In Resale Price Maintenance (RPM) contracts, the manufacturer specifies the resale price that retailers must charge to consumers. We study the role of using a RPM contract in a market where demand is influenced by retailer sales effort. First, it is well known that RPM alone does not provide incentive for the retailer to use adequate sales effort and some form of quantity fixing may be needed to achieve channel coordination. However, when the market potential of the product is uncertain, RPM with quantity fixing is a rigid contract form. We propose and study a variety of RPM contracts with quantity fixing that offer different forms of flexibility including pricing flexibility and quantity flexibility. Second, we address a long‐time debate in both academia and practice on whether RPM is anti‐competitive in a market when two retailers compete on both price and sales effort. We show that depending on the relative intensity of price competition and sales effort competition, RPM may lead to higher or lower retail prices compared to a two‐part tariff contract, which specifies a wholesale price and a fixed fee. Further, the impact of RPM on price competition and sales effort competition is always opposite to each other. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

挠性胶管动态特性参数的辨识

建立了挠性胶管的物理模型 .以此为基础提出了用系统辨识的方法识别其动态特性参数 ,并推导出相应的计算公式     

Opportunistic retooling of a flexible machine subject to failure

A set of jobs can be processed without interruption by a flexible machine only if the set of tools required by all jobs can be loaded in the tool magazine. However, in practice the total number of tools required by a job set would exceed the tool magazine capacity. In such situations, the job set has to be carefully partitioned at the start of the production run such that each partition can be processed without interruption. During the production run, if there are unscheduled machine downtimes due to machine failure, this provides an additional opportunity to optimally retool the magazine for a smaller job set consisting of just the unprocessed jobs. In this paper, we study job sequencing rules that allow us to minimize the total expected cost of machine down time due to machine failures and magazine retooling, assuming a dynamic re‐sequencing of the unprocessed jobs after each machine failure. Using these rules, we develop a branch‐and‐bound heuristic that allows us to solve problems of reasonable size. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 79–97, 2001     

虚拟环境下柔性线缆建模方法

研究了虚拟环境下柔性线缆建模方法，给出了离散控制点线缆简化模型和计算各离散控制点坐标值的算法。针对柔性线缆在虚拟环境中的碰撞检测问题，具体分析了从物体侧面绕过和从物体上表面经过2种碰撞检测情况，基于深度矢量和体矢量碰撞检测算法，给出了碰撞检测响应算法。     

树形挠性多体系统的 Kane 动力学方程 ——一种面向计算机的建模方法

本文针对具有树形结构的空间挠性多体系统,建立了Kane 动力学模型。挠性多体系统由任意数目的挠性体或刚体组成,假定铰链点之间具有三自由度相对转动和平动,选取相对平动速度和转动角速度,以及模态坐标的导数为系统的广义速度,建立了系统的最小维数运动方程,所得结果便于计算机自动生成和进行数值仿真。     

几何非线性机械臂有限段传递矩阵法

提出了几何非线性柔性机械臂动力学有限段传递矩阵建模方法,该方法既保留了有限段法自动计及几何非线性和动力刚化的优势,又保留了传递矩阵法建模的方便灵活.算例表明,计算时间与自由度成正比,在已知运动规律的情况下,可直接求得所需的驱动力矩,反之亦然.该方法无需建立系统总体动力学方程,易于工程技术人员掌握,具有一定实用价值.     

自适应平方根无迹滤波的雷达组网状态估计算法

从最小方差的角度分析了雷达组网无迹滤波(Unscented Filter,UF)状态估计的统计学本质,并且针对UF的Cholesky分解遇到非半正定矩阵容易发散、不准确滤波初值造成滤波发散以及异常扰动影响滤波效果等问题,提出将自适应平方根无迹滤波(Adaptive Square Root Unscented Filter,ASQUF)用于雷达组网状态估计,结合合理的滤波初始化策略,提高了UF的工程可用性。仿真验证表明,提出的ASRUF算法用于雷达组网空域目标状态估计时,初始化平稳无波动,工程可用性好,状态估计精度高,明显优于扩展卡尔曼滤波(Extended Kalman Filter,EKF)算法。