基于稀疏Bayes学习算法的无约束结构荷载重构方法北大核心CSCD

为快速准确重构含有未知初始条件的无约束结构外激励,提出了一种基于稀疏Bayes学习算法的荷载重构方法.结合函数拟合的思想建立控制方程,以噪声服从Gauss分布为先验,在Bayes模型中使用快速算法,稀疏重构未知荷载.为合理表达分段拟合中的初始条件,提出了改进的分段拟合手段,以上一分段末状态响应作为可能初始条件,并辅以低阶振型作为初始位移和初始速度的补充.算例以简化运载火箭模型为研究对象,考虑不同等级噪声和不同初始条件表达形式的影响,验证方法的精度和效率.     

基于自由度缩聚的最小秩修正损伤诊断

为了解决实测模态参数与有限元分析模态参数不匹配对损伤诊断精度影响的问题,推导了基于自由度缩聚法的残余力向量公式及最小秩修正公式.通过对结构自由度缩聚后的损伤前、后残余力向量的运算,可以得出相较于损伤前的残余力变化率向量,将残余力变化率向量元素的绝对值作为改进的残余力向量,通过运用推导出的改进残余力向量,能够较好地解决采用最小秩修正法时所选取模态个数必须等于待修正刚度矩阵秩这一矛盾,并由缩聚后最小秩修正公式计算出损伤程度.研究表明:在考虑噪音干扰下,改进的残余力向量法对自由度缩聚后的受损结构依然具有较高地识别精度.利用推导的最小秩修正公式进行损伤程度识别其结果是可靠的.本文所提方法既可以实现对实测自由度不完备结构的损伤定位,又可进行损伤程度的识别,具有较高的鲁棒性和损伤诊断性能.     

基于三次卷积插值的贝叶斯滤波方法研究

针对一类二维空间系统的状态估计模型,提出了一种用三次卷积插值方法递推估计的非线性滤波算法.仿真实例采用一个常用的非线性模型,并与粒子滤波算法进行对比分析,仿真结果表明三次卷积插值方法提高滤波估计精度,从而验证其估计一类状态估计模型解析解的可行性,其插值算法还可以推广到多维空间系统.     

相关噪声下多步无序量测状态估计更新算法

在多传感器系统中,由于通信时间的延迟性,常常会出现无序量测情况.为了提高估计精度,系统须对无序量测进行更新估计.状态估计更新算法是处理无序量测问题的一种有效方法.在过程噪声和量测噪声相关条件下,给出了含无序量测的传感器系统状态估计更新算法.仿真计算验证了该算法的有效性.     

多元数据融合在无人机结构健康监测中的应用

结构健康监测是保证航空器持续安全运行的重要方式,正成为无人机平台研发和适航认证的一项关键技术.针对无人机结构动态监测中的多种不同传感器测量信息,实时提取结构加速度、应变响应信号和模态特征参数,构造归一化的小波包能量变化率指标、应变能变化率指标、模态频率变化率指标与混合损伤评价指标,用于指示结构健康状态.利用多层次数据融合技术进行数据级融合、特征融合以及基于Bayes概率神经网络的决策融合,建立结构损伤程度、位置信息与健康评价指标之间的对应关系,通过粗糙集约简显著降低了特征属性的空间维度,获得关于结构健康状况的一致性决策.通过某型号无人机的健康监测实例验证了上述数据融合技术在识别多种类型传感器输入、多位置损伤识别中的精度,表明多元数据融合在无人飞行器结构损伤识别中的有效性.     

不完全观测下非线性非齐次随机系统的参数估计

本文研究不完全观测下非线性非齐次随机系统的参数估计问题. 首先, 通过构造扩展的Kalman滤波方程, 得到系统状态的次优估计, 并通过状态估计方程给出似然函数的表达式. 其次, 找到一个闭区间, 似然函数在这个闭区间上连续且在端点处取不到最大值. 最后, 当样本量足够大时, 运用Lepingle强大数定律和均方可积鞅强大数定律, 证明了极大似然估计量的存在性和强相合性.     

我国GDP的多步预测研究——基于Kalman滤波和泰勒展开的结合

将Kalman滤波应用于我国国内生产总值(GDP)和人均国内生产总值(GDPP)年度数据时,发现其多步预测的相对误差随着预测步长的增加而增大.为提高Kalman滤波多步预测的精度,在样本外预测中采用泰勒公式进行观测数据预报.实验结果表明泰勒公式的引入提高了Kalman滤波的多步预测精度.     

Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation北大核心CSCD

为解决模型参数不确定与外界干扰影响下,四旋翼无人机飞控作业中姿态与轨迹跟踪精度下降,反应迟缓的问题,利用拓展Kalman滤波应对非线性系统问题出色的适应能力和噪声抑制能力,对四旋翼状态信息进行初步估算来抑制高频信号干扰,从而降低了扩张状态观测器的估计负担.同时,与扩张状态观测器联合估计由系统不确定性参数与外界扰动联合组成的“总扰动”,使系统对于精确模型的依赖性降低,并利用扰动估计的微分值进行前馈补偿,以提高对突变扰动的跟踪精度,克服了突变干扰下的相位滞后现象.综合联合观测器、带前馈补偿的LESO及带误差补偿的PD控制律,形成了一种利用拓展Kalman滤波与前馈补偿后的扩张状态观测器联合观测扰动,能较大程度抑制高频噪声和突变扰动的改进型自抗扰控制器.仿真与实验结果表明,联合观测器能有效地减小观测误差幅值且能超前校正观测相位滞后,从而更好地得到更精确的状态信息,改进型自抗扰控制器能更好地满足四旋翼飞行器快速反应、高效稳定的控制要求,精准高效地完成复杂轨迹跟踪.     

主动约束阻尼开口柱壳的NLMS反馈减振控制

为缩减开口柱壳结构的振动,给出了一种局部主动约束阻尼（ALCD）敷设结构,并结合Lagrange方程和Sanders薄壳理论构建了压电耦合开口柱壳的动力学模型,根据推得的系统状态空间形式,应用归一化最小均方差自适应滤波算法（NLMS）和线性二次规划算法（LQR）设计了一种自适应反馈控制器,通过数值仿真研究了控制参数对开口柱壳中点动态特性和控制电压的影响.结果表明:NLMS反馈控制方法能在不同控制电压频率、滤波阶数和自适应步长下保证对开口柱壳减振的有效性;增加自适应步长和滤波阶数能进一步提高减振控制的响应速率,但会导致控制电压超调量增加,而取较大的滤波阶数和较高频率控制电压可以减小噪声扰动,增加控制系统的可靠性.     

用随机奇异值分解算法求解矩阵恢复问题

本文研究了大型低秩矩阵恢复问题.利用随机奇异值分解（RSVD）算法,对稀疏矩阵做奇异值分解.该算法与Lanczos方法相比,在误差精度一致的同时运算时间大大降低,且该算法对相对低秩矩阵也有效.     

Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach

An improved unscented Kalman filter approach is proposed to enhance online state of charge estimation in terms of both accuracy and robustness. The goal is to address the drawback associated with the unscented Kalman filter in terms of its requirement for an accurate model and a priori noise statistics. Firstly, Li-ion battery modelling and offline parameter identification is performed. Secondly, a sensitivity analysis experiment is designed to verify which model parameter has the greatest influence on state of charge estimation accuracy, in order to provide an appropriate parameter for the model adaptive algorithm. Thirdly, an improved unscented Kalman filter approach, composed of a model adaptive algorithm and a noise adaptive algorithm, is introduced. Finally, the results are discussed, which reveal that the proposed approach’s estimation error is less than 1.79% with acceptable robustness and time complexity.     

State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter

Accurate estimation of the battery state of charge (SOC) is of great significance for enhancing its service life and safety. In this study, based on the fractional-order equivalent circuit model of lithium-ion battery, the SOC estimation methods using dual Kalman filter (DKF) and dual extended Kalman filter (DEKF) are simulated and compared, in terms of model accuracy and SOC estimation accuracy. Then, combining the advantages of the DKF and DEKF algorithms, an SOC estimation algorithm based on adaptive double Kalman filter is proposed. This algorithm uses the recursive least squares (RLS) method to update the battery model parameters online in real time, and employs the DKF algorithm to filter the SOC twice to reduce the interferences from the battery model error and the current measurement error. In the experimental studies, the measured SOC values are compared with the estimated SOC values produced by the proposed algorithm. The comparison results show that SOC estimation error of the proposed algorithm is within the range of ±0.01 under most test conditions, and it can automatically correct SOC to true value in the presence of system errors. Thus, the validity, accuracy, robustness and adaptability of the proposed algorithm under different operation conditions are verified.     

The optimal robust finite-horizon Kalman filtering for multiple sensors with different stochastic failure rates

This work deals with the filtering problem for norm-bounded uncertain discrete dynamic systems with multiple sensors having different stochastic failure rates. For tackling the uncertainties of the covariance matrices of state and state estimation error simultaneously, their upper bounds containing a scaling parameter are derived, and then a robust finite-horizon filtering minimizing the upper bound of the estimation error covariance is proposed. Furthermore, an optimal scaling parameter is exploited to reduce the conservativeness of the upper bounds of the state and estimation error covariances, which leads to an optimal robust filtering for all possible missing measurements and all admissible parameter uncertainties. A numerical example illustrates the performance improvement over the traditional Kalman filtering method.     

Target torque estimation for gearshift in dual clutch transmission with uncertain parameters

The target torque of engaging clutches during gearshift is a key factor that affects the dynamic response of powertrains equipped with the dual clutch transmissions (DCT). This paper investigates a method to estimate the target torque of engaging clutches under conditions where engine torque and measurement signals contain white noise and some vehicle parameters (the radius of wheel and rolling friction coefficient) are uncertain. To compute the target torque accurately, the state of system should be estimated when the uncertain parameters exist. The vehicle powertrain is modeled as the 3DOF system when one clutch is closed and the 4DOF system when two clutches are open, while the measured signals include speeds of the engine, transmission, and vehicle (rotational speed of wheels). In addition to traditional extended Kalman filter (EKF), both the joint extended Kalman filter (JEKF) and dual extended Kalman filter (DEKF) are used to estimate the target torque. The simulation results show that DEKF and JEKF provide much higher accuracy in the estimation of target torque than EKF when some parameters of the model are uncertain, so as to produce a better ride performance of the transmission during gearshift, i.e. reduction of power interruption and compressed shifting time. Furthermore, the DEKF provides higher accuracy than the JEKF in estimating uncertain parameters.     

Nonlinear estimation applying an unscented transformation in systems with correlated uncertain observations

An approximation to the least squares filter is proposed for discrete signals whose evolution is governed by nonlinear functions, when the estimation is based on nonlinear observations with additive noise which can consist only of random noise; this uncertainty in the observation process is modelled by Bernoulli random variables which are correlated at consecutive time instants and are otherwise independent. The proposed recursive approximation is based on the unscented principle; successive applications of the unscented transformation to a suitable augmented state vector enable us to approximate the one-stage state and observation predictors from the state filter at the previous time instant. The performance of the proposed algorithm is compared with that of an extended algorithm in a numerical simulation example.     

Dynamic load identification for interval structures under a presupposition of ‘being included prior to being measured’

Influences of structural uncertainties in the dynamic load identification are always significant and need to be quantified. In case of insufficient information available, intervals are favorable for modelling uncertainties. To perform the interval propagation in an inverse problem, this paper develops a sequential dual-stage interval identification method under a presupposition that each noisy response, which is an accomplished measurement for reconstructing unknown loads, should be included in the corresponding interval response of the structure exerted by interval loads to be identified. The proposed method transforms the interval identification problem into a classical one at the midpoint of interval parameters and an optimization model for minimizing the radius of each interval load. The effectiveness of the proposed method is validated by a spatial truss subjected to multiple forces due to the inclusion of each unknown load in the corresponding load. Besides, regularized solutions without exact knowledge of the accuracy loss are recommended to be used as few as possible in the interval identification of unknown loads.     

结构非线性风振的路径积分方法

通过引入相关脉动风速滤波,将结构非线性风振方程转变为Ito随机微分方程的形式;该方程的解过程具有Markov性质．在时域内将状态方程展开,利用其瞬时线性化随机方程的解析解,基于路径积分给出了结构非线性风振响应概率密度的形式解,得到了一种分析结构非线性风振响应的新方法．对桅杆算例的数值分析表明,该方法较线性频域分析方法和非线性时域积分方法具有更好的准确性和有效性．     

自由漂浮空间非合作目标的运动预测

空间非合作目标的运动预测是航天器在轨服务中的一个重要问题。在获得非合作目标的运动预测结果后,追踪星即可规划运动轨迹以接近目标并对其进行捕获。该文提出了一种自由漂浮空间非合作目标的运动预测方法。该方法的核心思想是首先辨识出目标的姿态动力学参数和目标的质心运动学参数,然后利用参数辨识结果和目标的动力学方程实现对目标的运动预测。在姿态动力学参数的辨识过程中,首先对目标的惯性参数进行初步辨识,然后采用自适应无迹Kalman滤波器对姿态动力学参数进行粗略辨识,最后通过最优化方法进一步提高姿态动力学参数的辨识精度。该文通过数值仿真验证了所提运动预测方法的有效性。仿真结果表明,无论目标是做单轴旋转还是翻滚运动,所提运动预测方法都能够实现对目标的长时间高精度的运动预测。     

Leakage detection and location in gas pipelines through an LPV identification approach

A new approach to gas leakage detection in high pressure distribution networks is proposed, where two leakage detectors are modelled as a linear parameter varying (LPV) system whose scheduling signals are, respectively, intake and offtake pressures. Running the two detectors simultaneously allows for leakage location. First, the pipeline is identified from operational data, supplied by REN-Gasodutos and using an LPV systems identification algorithm proposed in [1]. Each leakage detector uses two Kalman filters where the fault is viewed as an augmented state. The first filter estimates the flow using a calculated scheduling signal, assuming that there is no leakage. Therefore it works as a reference. The second one uses a measured scheduling signal and the augmented state is compared with the reference value. Whenever there is a significant difference, a leakage is detected. The effectiveness of this method is illustrated with an example where a mixture of real and simulated data is used.