Optimal filtering for systems with finite-step autocorrelated noises and multiple packet dropouts

This paper addresses the optimal filtering problem for a class of uncertain dynamical systems with multiple packet dropouts and finite-step correlated observation noises. By rearranging the stochastic terms in the transmission and measurement matrices of the dynamical system into the noises directly, the process noises and observation noises in resulted system depend on the state as well as the stochastic uncertain perturbations, and are not only autocorrelated respectively but also cross-correlated. For this complicated dynamical system, instead of designing a Kalman-type filter, a globally optimal filtering in the minimum mean square error sense is developed by exploiting sufficiently the statistical properties of correlated noises. Numerical simulation is provided to demonstrate the performance of the proposed filter.     

Polynomial networks based adaptive attitude tracking control for NSVs with input constraints and stochastic noises

This paper proposes a backstepping technique and Multi-dimensional Taylor Polynomial Networks (MTPN) based adaptive attitude tracking control strategy for Near Space Vehicles (NSVs) subjected to input constraints and stochastic input noises. Firstly, considering the control input has stochastic noises, and the attitude motion dynamical model of the NSVs is actually modeled as the Multi-Input Multi-Output (MIMO) stochastic nonlinear system form. Furthermore, the MTPN is used to estimate the unknown system uncertainties, and an auxiliary system is designed to compensate the influence of the saturation control input. Then, by using backstepping method and the output of the auxiliary system, a MTPN-based robust adaptive attitude control approach is proposed for the NSVs with saturation input nonlinearity, stochastic input noises, and system uncertainties. Stochastic Lyapunov stability theory is utilized to analysis the stability in the sense of probability of the entire closed-loop system. Additionally, by selecting appropriate parameters, the tracking errors will converge to a small neighborhood with a tunable radius. Finally, the numerical simulation results of the NSVs attitude motion show the satisfactory flight control performance under the proposed tracking control strategy.     

Joint parameter and state estimation for stochastic uncertain system with multivariate skew t noises

Due to the pulse interference, measurement outliers and artificial modeling errors, the multivariate skew t noise widely exists in the real environment. However, to date, little attention has been paid to the state estimation for systems in which the process noise and the measurement noise are both modeled as the heavy-tailed and skew non-Gaussian noise. In this paper, the multivariate skew t distribution is utilized to model the heavy-tailed and skew non-Gaussian noise. Then a probabilistic gra...     

Observer-type Kalman innovation filter for uncertain linear systems

An observer-type of Kalman innovation filtering algorithm to find a practically implementable "best" Kalman filter, and such an algorithm based on the evolutionary programming (EP) optima-search technique, are proposed, for linear discrete-time systems with time-invariant unknown-but-hounded plant and noise uncertainties. The worst-case parameter set from the stochastic uncertain system represented by the interval form with respect to the implemented "best" filter is also found in this work for demonstrating the effectiveness of the proposed filtering scheme. The new EP-based algorithm utilizes the global optima-searching capability of EP to find the optimal Kalman filter and state estimates at every iteration, which include both the best possible worst case Interval and the optimal nominal trajectory of the Kalman filtering estimates of the system state vectors. Simulation results are included to show that the new algorithm yields more accurate estimates and is less conservative as compared with other related robust filtering schemes     

鲁棒EKF在脉冲星导航系统中的应用

针对脉冲星导航系统的滤波问题,传统的扩展卡尔曼滤波(EKF)算法存在不能克服系统模型存在不确定性参数以及乘性噪声等缺陷,提出一种鲁棒EKF算法。首先,分析了状态预测误差方程和估计误差方程,利用统计学原理,得到了状态预测方差矩阵和状态估计方差矩阵计算等式。由于系统模型存在不确定性参数,状态预测协方差矩阵和状态估计协方差矩阵无法计算;因此,利用4个重要矩阵不等式,分析并找到预测方差矩阵和状态估计方差矩阵的上界。最后,利用状态估计误差协方差矩阵上界设计状态增益矩阵,使得状态估计协方差矩阵的迹最小。将该算法对脉冲星导航系统进行仿真,仿真结果验证了所提算法的有效性。     

Adaptive robust cubature Kalman filtering for satellite attitude estimation

This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms, one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter.     

离散不确定系统的优化鲁棒滤波方法及在飞行试验中的应用

提出了一种离散系统的优化鲁棒滤波方法。为了得到滤波的逼近计算式，通过优化加权矩阵得到了上界不等式逼近和等效系统矩阵，得到了鲁棒滤波的时间更新算法；通过优化加权矩阵得到了下界不等式逼近和等效观测矩阵，得到了鲁棒滤波的测量更新算珐，并且给出了鲁棒滤波算法收敛的条件。飞行试验数据处理的结果表明，提出的方法是有效的。     

混合高斯测量噪声下的多模型滤波

就带有混合高斯测量噪声的离散时间系统 ,提出了一种简化的多模型滤波。理论分析证明该滤波方法用较少的计算量得到了与交互多模型滤波相同的估计性能。为满足应用要求 ,给出了该滤波器的数值鲁棒实现方法。一个关于仅有方位测量的制导例子验证了该滤波算法的有效性。     

自适应无迹增量滤波方法

提出自适应无迹增量滤波(AUIF)的概念和定义,建立自适应无迹增量滤波模型及其分析方法,给出递推算法.传统的滤波方法极少关注量测方程的系统误差.在许多实际情况(如深空探测),量测方程由于受环境因素及测量设备不稳定等影响往往无法进行验证或校准而存在未知的系统误差,并且模型参数和噪声统计量也具有不确定性.这种不确定性会使递推过程产生较大误差,甚至导致发散,从而降低滤波精度.提出的AUIF能够成功消除这种未知的系统误差,也能够实时估计变化的噪声统计量,提高滤波精度.该方法计算简单,便于工程应用.      

飞机纵向非线性系统的鲁棒输出跟踪控制器设计

研究了单输入单输出非线性不确定系统的鲁棒输出跟踪控制。在标称系统可输入/输出线性化、不确定性项有界且满足广义匹配条件的情况下,可得到系统的高增益鲁棒输出跟踪控制器。鲁棒控制器仅依赖于设计参数和不确定性的界。将该方法应用于飞机纵向高阶非线性动态的控制器设计,并进行了数字仿真。结果表明了该方法的有效性。     

飞行数据处理的非线性鲁棒滤波技术

研究了飞机飞行试验数据处理中的非线性鲁棒滤波技术,分析给出了实际飞行条件了一种飞机运动的新模型,它是含不确定参数项的非线性模型,同时给出了该不确定参数项的解析的表达形式,然后研究了对应于此类非线不确定系统的鲁棒滤波算法,得到了该滤波问题的充分解,并对该解进行扩展线性化处理,得么了一个较易实现的扩展鲁棒滤波算法,最后利用该算法对实际的数据进行了处理,结果表明,所提出的算法是切实可行的。     

卫星姿态跟踪系统的鲁棒控制器设计

研究了具有参数不确定性和外部干扰的卫星姿态跟踪控制问题。针对这一类多输入／多输出不确定非线性系统，提出了一个基于不确定项上界的鲁棒输出跟踪控制器设计方法。应用输入／输出反馈线性化法和李亚普诺夫方法，设计了一个控制律，它可确保系统输出按指数规律跟踪期望输出。该控制器计算简单，易于实现。仿真结果表明：即使系统存在不确定性，仍可在闭环系统中实现精确的姿态控制。     

Efficient recursive state estimator for dynamic systems withoutknowledge of noise covariances

An efficient recursive state estimator for dynamic systems without knowledge of noise covariances is suggested. The basic idea for this estimator is to incorporate the dynamic matrix and the forgetting factor into the least squares (LS) method to remedy the lack of knowledge of noises. We call it the extended forgetting factor recursive least squares (EFRLS) estimator. This estimator is shown to have similar asymptotic properties to a completely specified Kalman filter state estimator. More importantly, the performance of EFRLS greatly exceeds that of existing filtering techniques when the noise variance is misspecified. In addition, EFRLS also performs well when there is cross-correlation between the process and measurement noise streams or temporal dependencies within those streams. Some discussions and a number of simulations are made to provide practical guidance on the choice of an optimal forgetting factor and evaluate the performance of the EFRLS algorithms, which strongly dominates that of the standard forgetting factor recursive least squares (FRLS) and some misspecified Kalman filtering     

高超声速飞行器组合导航鲁棒滤波算法

高超声速飞行器由于其飞行环境的影响,使得GPS和星敏感器的量测噪声表现出非高斯特性。针对常规基于Kalman滤波的组合导航在非高斯噪声下性能下降的问题,提出了基于鲁棒滤波的高超声速飞行器组合导航算法。方法在惯性／GPS／＆异步量测建模的基础上,通过随机去耦将量测更新转化为线性回归问题,并基于M估计获得状态量最优估计。仿真结果表明,方法对非高斯噪声具有更好的鲁棒性,有效提高了高超声速飞行器组合导航系统的性能。     

基于LMI的鲁棒滤波在无人机飞控系统中的应用

针对无人机在实际飞行过程中存在外界干扰以及传感器量测噪声的问题,应用线性矩阵不等式理论对无人机飞控系统进行了降阶鲁棒滤波器设计.讨论了适用于连续系统的降阶鲁棒滤波算法以及滤波器存在的条件.最后,进行了无人机纵向高度保持阶段的数字仿真,给出迎角与俯仰角速度的仿真曲线,仿真结果验证了该算法的合理性和有效性.     

基于卡尔曼滤波的航空发动机单神经元自适应控制

针对航空发动机多任务、多变量、高精度和一体化控制的需求,提出了一种基于卡尔曼滤波的单神经元自适应控制方法。该方法在单神经元自适应控制算法的基础上,增加了对控制量和发动机反馈量的滤波,提高了响应速度,精度较高。仿真结果证明,该方法对过程噪声和测量噪声具有很强的克服能力,所需计算量较小,能满足发动机控制对实时性的要求。     

The Tracking Index: A Generalized Parameter for ? ? ? and ? ? ? ? Target Trackers

A generalized, optimal filtering solution is presented for the target tracking problem. Applying optimal filtering theory to the target tracking problem, the tracking index, a generalized parameter proportional to the ratio of the position uncertainty due to the target maneuverability to that due to the sensor measurement, is found to have a fundamental role not only in the optimal steady-state solution of the stochastic regulation tracking problem, but also in the track initiation process. Depending on the order of the tracking model, the tracking index solution yields a closed form, consistent set of generalized tracking gains, relationships, and performances. Using the tracking index parameter, an initializing and tracking procedure in recursive form, realizes the accuracy of the Kalman filter with an algorithm as simple as the well-known ? ? ? filter or ? ? ? ? ? filter depending on the tracking order.     

一种非线性滤波器的误差分析及应用

针对一类具有未建模误差和扰动的非线性系统的状态估计问题,提出一种在线估计并补偿模型误差的非线性滤波算法,该算法利用非线性预测滤波(NPF)基于预测输出残差的方差最小的基本原则估计模型误差,冉利用扩展卡尔曼滤波(EKF)的思想对补偿后的模型进行状态估计;详细推导了状态估计误差及其方差阵的传播模型.以卫星姿态确定系统为例,...     

基于广义预测控制的网络控制系统设计

网络控制系统中,网络诱导时延和数据包丢失会导致系统控制性能的下降甚至不稳定。针对上述问题,提出了一种基于广义预测控制的控制方法。该方法采用时间戳机制和模型的在线辨识,能够根据测量系统输出及时对预测值进行在线修正。在执行器端设计了网络延迟补偿器,可根据当前时刻的最新控制信号对网络系统实现有效控制。仿真结果验证了该方法的有效性。     

Spacecraft attitude/rate estimation using vector-aided GPSobservations

A sequential filtering algorithm is presented for spacecraft attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the state of the filter, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the robustness and accuracy of the method. Numerical examples are used to demonstrate the performance of the method