连续短线段空间圆弧转接与插补

针对连续短线段高速加工控制需求,研究了利用空间圆弧转接实现高速插补的方法。建立了圆弧转接数学模型,研究了转接的几何约束和运动约束、转接参数计算、空间圆弧插补等相关理论与算法,并详细分析了算法的使用范围、效率制约因素和计算量等工程实现相关问题。仿真和加工实验表明:该转接算法能显著提高加工效率,减小振动,改善工件表面质量。作为空间圆弧转接算法基础的空间圆弧插补算法对空间任意平面内圆弧插补具有实用价值。本文所有算法已经在自主研发的数控平台上进行了验证,应用效果良好。     

Ⅱ型PIO反馈与前馈抑制系统对比

 当舵机速率限制饱和时会产生附加时延,恶化飞行品质,触发Ⅱ型驾驶员诱发振荡（PIO）。运用描述函数法研究速率限制反馈（RLF）和死区增稳（DASA）Ⅱ型PIO抑制系统的开环相位补偿能力,提出RLF抑制系统的参数优化选择方法,分析了DASA抑制系统中死区和滤波器参数对相位补偿能力的影响。研究了两型抑制系统对正弦与偏差输入信号的开环响应特点。建立了含速率限制器的人机系统数学模型,基于描述函数法探究了Ⅱ型PIO的产生机理,推导了抑制Ⅱ型PIO发生的公式,应用连续信号相位补偿法抑制Ⅱ型PIO的发生。对阶跃、离散和正弦3种易于诱发PIO现象的跟踪任务进行数值仿真,对比了两种抑制系统的Ⅱ型PIO抑制能力。结果表明,反馈型RLF抑制系统对Ⅱ型PIO的抑制能力强于前馈型DASA抑制系统,有效降低了Ⅱ型PIO发生的可能性。     

科氏质量流量计全数字闭环系统的设计与实现

科氏质量流量计(CMF,Coriolis Mass Flowmeter)全数字闭环系统,采用现场可编程门阵列(FPGA,Field Programmable Gate Array)和现代数字信号处理方法对CMF传感器进行稳定精确的闭环控制,实时性和精度较高.以高速并行器件FPGA为运算和控制核心,在相位控制中引入FIFO(First In First Out)组件,通过控制FIFO的读、写请求信号来改变时间差,实现对拾振和激励信号的相位差准确、稳定的控制;采用不连续和连续幅值控制相结合的非线性幅值控制方法,快速、准确地设定幅值,适应性强,实现对拾振信号幅值的良好控制,并控制拾振信号以稳定的幅值输出,提高CMF的测量精度和稳定性.实流标定对比实验结果表明:CMF数字闭环在零点稳定性、动态响应特性和重复性方面都优于模拟闭环,并在一定程度上提高了CMF的测量精度.      

复杂环境下考虑动力学约束的翼伞轨迹规划

翼伞系统具有大惯性、强非线性等特征,而基于传统质点模型所规划的目标轨迹难以满足复杂环境下的系统动力学约束,因此在轨迹规划中采用高自由度动力学模型也就成为了计算翼伞真实运动轨迹的必然趋势。然而,翼伞的动力学模型更加复杂,目前迫切需要解决的问题就是保证规划轨迹平滑、稳定。针对该问题,本文将建立精确的翼伞六自由度动力学模型,将其引入翼伞归航的轨迹规划中,并通过改进高斯伪谱法,设计一种基于分段点规划、离散点初次规划、离散点自重构的三阶轨迹优化策略。仿真结果表明,所提算法可解决传统算法在应用动力学模型后难以得到稳定轨迹的问题,并实现复杂环境下的精确地形规避,确保规划轨迹满足翼伞的非线性动力学约束。     

舰载武器惯导传递对准技术分析

舰船惯性大,加之受海水阻力和风力的影响,其运动态势较空中平台和陆地平台特殊,因此舰载武器系统惯导传递对准特性与机载、车载武器系统的惯导传递对准特性不同.通过对舰载武器系统惯导传递对准典型匹配方式的对比研究,结合舰船的运动特性及动态干扰特性,给出舰载武器系统惯导传递对准匹配方式选择原则.     

MEMS陀螺随机误差的建模与分析

为了更全面地了解微机电系统(MEMS,Micro-Electro-Mechanical Systems)陀螺仪的随机漂移误差随时间变化的特性,利用动态Allan方差分析法对MEMS陀螺仪输出信号特性进行了全面分析.首先介绍了Allan方差和动态Allan方差分析法原理,然后分别利用Allan方差分析法和动态Allan方差分析法对MEMS陀螺仪的实测数据进行了特性研究与性能分析.研究结果表明:速率斜坡、量化噪声和速率随机游走是MEMS陀螺的主要随机噪声,并且MEMS陀螺的随机漂移具有随时间变化的不稳定性.动态Allan方差不仅可以分离和辨识出MEMS陀螺的主要随机误差源,而且可以跟踪和描述信号随时间变化的稳定性,因此动态Allan方差较经典Allan方差分析法能够更全面地表征MEMS陀螺仪的性能.     

交替LU分裂算法及其在CFD中的应用

在CFD(Computational Fluid Dynamics)时间相关算法中,为了保证计算的稳定性,时间步长的取值通常会很小,这将导致计算过程收敛缓慢.针对这一问题,提出了一种新的迭代算法—交替LU分裂(ALUS,Alternating Lower-Upper Splitting)算法,可以有效加速收敛,提高计算效率.ALUS算法将系数矩阵分裂成上、下三角矩阵,因此仅需要利用追赶法求解两个三角矩阵,计算量较小,容易实现.给出了ALUS算法收敛的定理,并且通过线性问题以及CFD圆柱绕流的数值模拟对ALUS算法进行了检验.理论分析和数值实验的结果均表明:ALUS算法计算量小,大大节省了计算时间,而且该算法是鲁棒的.因此ALUS算法是高效的、稳定的算法,适用于CFD数值模拟.     

基于多体动力学的旋翼模型与气弹稳定性

针对无轴承等先进旋翼结构特点,建立了基于柔性多体系统动力学方法的旋翼气弹分析模型.旋翼各构件建模相互独立,便于组成不同构型旋翼,适合于新构型旋翼气弹分析.分析模型集成了适合于旋翼气弹分析的隐式大变形桨叶模型,构件动力学方程增加了铰链动力学方程,统一约束方程形式,改进了角运动约束方程,避免连续旋转引起的奇点问题.应用分析模型计算无铰式旋翼和无轴承旋翼的气弹稳定性,分析结构参数对旋翼气弹稳定性的影响.分析结果表明模型能准确计算结构弹性变形的耦合及非线性,提高旋翼气弹稳定性分析精度.      

Reliability Analysis of Aircraft Condition Monitoring Network Using an Enhanced BDD Algorithm

The aircraft condition monitoring network is responsible for collecting the status of each component in aircraft. The reliability of this network has a significant effect on safety of the aircraft. The aircraft condition monitoring network works in a real-time manner that all the data should be transmitted within the deadline to ensure that the control center makes proper decision in time. Only the connectedness between the source node and destination cannot guarantee the data to be transmitted in time. In this paper, we take the time deadline into account and build the task-based reliability model. The binary decision diagram (BDD), which has the merit of efficiency in computing and storage space, is introduced when calculating the reliability of the network and addressing the essential variable. A case is analyzed using the algorithm proposed in this paper. The experimental results show that our method is efficient and proper for the reliability analysis of the real-time network.     

A Novel PF-LSSVR-based Framework for Failure Prognosis of Nonlinear Systems with Time-varying Parameters

Particle filtering (PF) is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the traditional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR framework based on PF and least squares support vector regression (LSSVR) for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction uncertainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life (RUL) is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.