Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Modified filter for mean elements estimation with state jumping

To investigate the real-time mean orbital elements(MOEs)estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit trans-fer,a modified augmented square-root unscented Kalman filter(MASUKF)is proposed.The MASUKF is composed of sigma points calculation,time update,modified state jumping detec-tion,and measurement update.Compared with the filters used in the existing literature on MOEs estimation,it has three main characteristics.Firstly,the state vector is augmented from six to nine by the added thrust acceleration terms,which makes the fil-ter additionally give the state-jumping-thrust-acceleration esti-mation.Secondly,the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency.Thirdly,when sate jumping is detected,the covariance matrix inflation will be done,and then an extra time update process will be con-ducted at this time instance before measurement update.In this way,the relatively large estimation error at the detection moment can significantly decrease.Finally,typical simulations are per-formed to illustrated the effectiveness of the method.

Command filtered integrated estimation guidance and control for strapdown missiles with circular field of view

In this paper,an integrated estimation guidance and control(IEGC)system is designed based on the command filtered backstepping approach for circular field-of-view(FOV)strapdown missiles.The threedimensional integrated estimation guidance and control nonlinear model with limited actuator deflection angle is established considering the seeker's FOV constraint.The boundary time-varying integral barrier Lyapunov function(IBLF)is employed in backstepping design to constrain the body line-of-sight(BLOS)in IEGC system to fit a circular FOV.Then,the nonlinear adaptive controller is designed to estimate the changing aerodynamic parameters.The generalized extended state observer(GESO)is designed to estimate the acceleration of the maneuvering targets and the unmatched time-varying disturbances for improving tracking accuracy.Furthermore,the command filters are used to solve the"differential expansion"problem during the backstepping design.The Lyapunov theory is used to prove the stability of the overall closed-loop IEGC system.Finally,the simulation results validate the integrated system's effectiveness,achieving high accuracy strikes against maneuvering targets.

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.

Bayesian estimation for nonlinear stochastic hybrid systems with state dependent transitions

The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems. However, most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov jump system, few liter- ature is related to the estimation problem of nonlinear stochastic hybrid systems with state dependent transitions. According to this problem, a new methodology which relaxes quite a restrictive as- sumption that the mode transition process must satisfy Markov properties is proposed. In this method, a general approach is presented to model the state dependent transitions, the state and output spaces are discreted into cell space which handles the nonlinearities and computationally intensive problem offline. Then maximum a posterior estimation is obtained by using the Bayesian theory. The efficacy of the estimator is illustrated by a simulated example .

State estimation of connected vehicles using a nonlinear ensemble filter

The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs(on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter(En KF) is introduced to estimate the vehicle's state with observations from navigation satellites and neighborhood vehicles, and the original En KF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in En KF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.

Nonlinear Bayesian Estimation： From Kalman Filtering to a Broader Horizon

This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date,one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective,which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics(e.g., mean and covariance) conditioned on a system's measurement data.This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering(KF)techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation.

Observer-Based Nonlinear Control of A Torque Motor with Perturbation Estimation

This paper presents an observer-based nonlinear control method that was developed and implemented to provide accurate tracking control of a limited angle torque motor following a 50Hz reference waveform. The method is based on a robust nonlinear observer, which is used to estimate system states and perturbations and then employ input-output feedbazk linearization to compensate for the system nonlinearities and uncertainties. The estimation of system states and perturbations allows input-output linearization of the nonlinear system without an accurate mathematical model of nominal plant. The simulation results show that the observer-based nonlinear control method is superior in comparison with the conventional model-based state feedback linearizing controller.

Comparison of Linearized Kalman Filter and Extended Kalman Filter for Satellite Motion States Estimation

The performance of the conventional Kalman filter depends on process and measurement noise statistics given by the system model and measurements.The conventional Kalman filter is usually used for a linear system,but it should not be used for estimating the state of a nonlinear system such as a satellite motion because it is difficult to obtain the desired estimation results.The linearized Kalman filtering approach and the extended Kalman filtering approach have been proposed for a general nonlinear system.The equations of satellite motion are described.The satellite motion states are estimated,and the relevant estimation errors are calculated through the estimation algorithms of the both above mentioned approaches implemented in Matlab are estimated.The performances of the extended Kalman filter and the linearized Kalman filter are compared.The simulation results show that the extended Kalman filter is much better than the linearized Kalman filter at the aspect of estimation effect.

ESTIMATE ACCURACY OF NONLINEAR COEFFICIENTS OF SQUEEZEFILM DAMPER USING STATE VARIABLE FILTER METHOD

The estimate model for a nonlinear system of squeeze film damper (SFD) is described.The method of state variable filter (SVF) is used to estimate the coefficients of SFD.The factors which are critical to the estimate accuracy are discussed

基于非线性状态估计(NSET)方法的燃机电厂故障检测研究及应用

随着“双碳”目标的提出,燃气轮机发电机组的经济、可靠运行成为了行业关注的重点。因此,准确检测故障和提高运行水平具有重要的研究价值。本文针对燃机电厂的故障检测问题,结合小波去噪等数据预处理方法,提出了一种基于非线性状态估计(NSET)的故障检测方法。该方法利用电力大数据,首先构建记忆矩阵并建立数学模型,然后通过比较实际曲线与预测曲线,分析残差值,从而有效地检测出故障。为了验证其有效性,本文以浙江某燃机电厂为例,在仿真平台和生产现场分别进行实验。实验结果表明,该方法能够有效检测33种故障也能够检测生产现场的燃机速比阀故障、热电偶故障等常见故障。最后,本文将该方法落地到模拟数字电厂项目中,实现了数字电厂故障检测技术的工程示范应用。

Recursive weighted least squares estimation algorithm based on minimum model error principle

Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.

Estimation, Intervention and Interaction of Multi-agent Systems

在这篇论文，我们在关于多代理人系统的最近的研究上提供简短调查。也就是，我们在研究的三个区域集中于结果评价并且过滤，由外部工具的干预，和交互控制。

An Approximate High Gain Observer for Speed-sensorless Estimation of Induction Motors

Rotor speed estimation for induction motors is a key problem in speed-sensorless motor drives. This paper performs nonlinear high gain observer design based on the full-order model of the induction motor. Such an effort appears nontrivial due to the fact that the full-model at best admits locally a non-triangular observable form(NTOF), and its analytical representation in the NTOF can not be obtained. This paper proposes an approximate high gain estimation algorithm, which enjoys a constructive design, ease of tuning, and improved speed estimation and tracking performance. Experiments demonstrate the effectiveness of the proposed algorithm.

基于NSET的风力发电机后轴承温度预警方法

采用非线性状态估计(NSET)方法对风力发电机的后轴承温度进行建模与分析;并采用Relief方法对特征参数进行选择,减少了NSET建模的复杂性。通过所建模型预测发电机后轴承温度,将其与正常运行温度进行比较;并利用滑动窗口对残差均值进行比较。若超出阈值范围,则发电机出现故障。验证结果表明,非线性状态估计(NSET)方法可以准确预测发电机后轴承温度,实现发电机运行状态的预警监测。

平面环境约束辅助的单目视觉惯性里程计

为提高视觉惯性导航系统在宽动态、长航时、大范围作业环境中的精确性与鲁棒性,提出了一种平面环境约束辅助的单目视觉惯性里程计。通过在视频图像中提取并跟踪均匀分布的FAST特征点,并采用对称光流剔除误跟踪点,实现了视觉特征点高效检测与精确跟踪;无需计算稠密深度地图,仅从稀疏特征点集中检测共面特征点,拟合空间平面,构建了对视觉特征点三维坐标的空间几何约束;融合视觉特征点重投影误差、共面特征点坐标约束以及惯性测量单元(Inertial measurement unit,IMU)预积分误差构造代价函数,采用非线性优化方法估计了系统状态。最后,分别在公开数据集和实际户外场景中评估了算法的准确性和有效性。实验结果表明,相较于VINS-Mono和ORB-SLAM3算法,本文方法的定位结果有显著提升,实现了复杂应用环境中精确、稳定导航,在机器人、无人驾驶等领域具有较大的实用价值。

量测随机丢失下基于容积卡尔曼滤波的厚尾噪声处理方法

针对量测随机丢失和厚尾量测噪声条件下的非线性状态估计易发散问题,提出了一种新的非线性卡尔曼滤波方法。引入服从Gamma分布的辅助参数,将厚尾量测噪声建模为Student’s t分布,以解决厚尾噪声导致的状态估计易发散问题,并采用服从Benroulli分布的随机变量来描述量测信号随机丢失的现象;在量测随机丢失下,基于目标状态和未知参数建立联合后验分布,并使用变分贝叶斯方法,联合估计系统状态、量测丢失概率和未知的厚尾噪声。非线性目标跟踪仿真实验表明,提出的算法可自适应估计未知的量测丢失概率,在野值概率为5%的条件下,算法目标跟踪的位置、速度和转动速率均方根误差分别为对比算法的37%、28%和60%;在野值概率为10%的条件下,其他算法均出现了发散现象,而提出的算法依然能够以较低的误差跟踪目标,体现了所提算法良好的鲁棒性和优越性。

随机丢包的无迹卡尔曼滤波估计以及性能分析

在工程应用中,无线网络控制系统多为非线性系统。由于长距离传输和通信网络不可靠等原因,系统传感器的测量值可能在传输过程中丢失,影响精度估计以及系统性能。文中研究一类受相关噪声和传感器测量值丢失影响的非线性离散随机系统的无迹卡尔曼滤波问题。通过引入一个服从伯努利分布且条件概率已知的随机变量来描述随机发生的传感器测量值丢失现象。文中提出了一种对数据进行补偿的算法,并使用标准数值软件对所得结果进行验证。结果表明,经过算法补偿后的滤波能够较好地估计系统,可以减少传感器测量值丢失对滤波器性能的影响,增加估计的准确性。

基于多源量测的主配网一体化状态估计方法

针对主配网结构复杂且测量设备无法全节点覆盖的问题,本文基于多源量测数据提出了主配网一体化的状态估计算法。针对不同的量测条件,设计相应的状态估计模型,构建非线性动力学系统以保证状态估计算法的全局收敛性。同时,考虑主配网状态估计的协调问题,融合更新频率高的数据采集与监视控制系统,同步相量测量装置量测和更新频率低的高级计量架构量测,开展主配一体化的状态估计方案设计。在IEEE标准算例基础上改进的主配网络上对该方法进行相关测试,仿真结果验证了所提算法的有效性。

非线性状态估计(NSET)建模方法在故障预警系统中的应用

工业生产的特点是设备多且长时间不间断运行,设备运行启停耗量巨大,生产运行实时数据量巨大且密集,设备事故或停产常常会带来巨大的经济损失,因此,设备故障预警在工业生产过程中起到了至关重要的作用。本文概述了非线性状态估计建模方法的原理,通过简单仿真示例说明此方法可行。举例说明了此建模方法在电厂预警系统上的应用,并用该方法对电厂送风机设备的出口压力预警仿真,当残差值超出预先设定的时,发出预警信息,提示运行人员检查设备状态。