具有有限连续丢包网络控制系统的最优线性估值器

本文研究了观测数据和控制输入数据传输具有有限连续丢包的线性离散随机系统的最优估计问题.利用两个满足Bernoulli分布的随机变量来分别描述从传感器到估值器和从控制器到执行器之间的数据丢包现象.通过引入两组新的变量,将原系统转化为一个带有随机参数的系统.利用射影理论,提出了线性最小方差最优线性估值器,包括滤波器、预报器和平滑器.最后研究了稳态线性估值器,并给出了稳态存在的一个充分条件.仿真例子验证了算法的有效性.     

具有一步随机滞后和多丢包的网络系统的最优线性估计

研究了具有随机时滞和丢包的网络系统的最优线性估计问题.本文通过两个满足 Bernoulli分布的随机变量来描述网络数据传输中可能存在的一步随机滞后和多丢包现象. 并基于新息分析方法,提出了线性最小方差下的最优线性状态滤波器、预报器和平滑器. 它们通过解一个Riccati方程和一个Lyapunov方程得到.最后,给出了稳态估值器存在的一个充分条件. 并通过仿真例子验证其有效性.     

具有随机多丢包的ARMA信号最优线性估计

研究网络环境下具有随机丢包的自回归滑动平均(ARMA)信号的估计问题,其中丢包现象通过一个满足Bernoulli分布的随机变量描述.通过ARMA模型与状态空间模型的转化,将具有丢包的ARMA信号估计问题转化为具有丢包的状态空间模型的状态和白噪声估计问题.利用射影理论分别给出线性最小方差最优线性状态估值器和白噪声估值器,进而获得ARMA信号估值器.仿真结果表明,当存在数据丢失时,所提出的算法与以往基于完整数据的最优估计算法相比具有最优性和有效性.     

具有传感器增益退化、随机时延和丢包的分布式融合估计器

研究了具有传感器增益退化、模型不确定性、数据传输时延和丢包的网络化多传感器分布式融合估计问题,模型的不确定性描述为系统矩阵受到随机扰动,传感器增益退化现象通过统计特性已知的随机变量来描述,随机时延和丢包现象存在于局部最优状态估计向融合中心传输的过程中.首先,设计了一种局部最优无偏估计器,然后将传输时延描述为随机过程,并在融合中心端建立符合存储规则的时延-丢包模型,利用最优线性无偏估计方法,导出最小方差意义下的分布式融合估计器.最后,通过算例仿真证明所设计融合估计器的有效性.     

具有数据包丢失的离散随机不确定系统的线性最优满阶估值器

研究了具有数据包丢失和随机不确定性离散随机线性系统的状态估计问题.其中数据包丢失是随机的,且满足Bernoulli分布,系统矩阵中的随机不确定性由一个白色乘性噪声来描述.首先,通过配方方法,提出了最小均方意义下的无偏最优线性递推满阶滤波器.所提出的滤波器用到了当前时刻和最近时刻接收到的观测来保证线性最优性.与多项式滤波和增广滤波器相比,本文的滤波器具有较小的计算负担.然后,基于所获得的线性滤波器推导了线性最优预报器和平滑器.进一步研究了线性最优估值器的渐近稳定性,给出了稳态特性存在的一个充分条件.最后,通过两个仿真例子验证了所提估计算法的优越性.     

具有噪声方差及多种网络诱导不确定系统鲁棒Kalman估计

对不确定噪声方差乘性噪声,同时带观测缺失、丢包和一步随机观测滞后三种网络诱导特征的混合不确定网络化系统,应用带虚拟噪声的扩维方法和去随机参数方法,将其转化为带不确定虚拟噪声方差的时变系统.基于极大极小鲁棒估计原理,对带虚拟噪声方差保守上界的最坏情形系统,设计了鲁棒时变和稳态Kalman估值器.对所有容许的不确定性,保证实际Kalman估计误差方差有最小上界.应用扩展的Lyapunov方程方法和矩阵分解方法证明了所设计估值器的鲁棒性.证明了实际和保守估值器的精度关系,以及时变和稳态估值器间的按实现收敛性.应用于F-404航空发动机系统的仿真验证了所提出结果的正确性和有效性.     

离散随机奇异系统的最优融合全阶状态估值器

研究带多传感器和相关观测噪声的离散随机奇异系统的分布式融合状态估计问题.核心思想是将带多传感器的随机奇异系统转化为一个等价的非奇异系统组.在得到局部非奇异系统的状态估计后,利用线性最小方差意义下的最优加权融合算法,得到原系统的全阶最优融合滤波器和平滑器.仿真算例表明,融合估值器优于每个局部估值器.     

基于Kalman滤波的通用和统一的白噪声估计方法

用射影理论,基于Kalman滤波提出了通用和统一的白噪声估计方法,可统一解决带非零均值相关噪声的线性离散时变随机控制系统的白噪声滤波、平滑和预报问题.提出了输入白噪声估值器和观测白噪声估值器,最优和稳态白噪声估值器,固定点、固定滞后和固定区间白噪声平滑器,白噪声新息滤波器和Wiener滤波器.它可应用于石油地震勘探信号处理和状态估计,为解决信号和状态估计问题,提供了新的途径和工具.关于Bernoulli-Gaussian白噪声估值器的仿真例子说明了其有效性.     

奇异线性系统最优估计的多项式方法

基于多项式ARMA新息模型方法提出了随机奇异线性离散时间系统的稳态最优估计.估值器的增益矩阵是通过新息分析和射影方法推得;其计算归结为求解一个多项式方程和谱分解.这一结果是最优估计多项式方法在奇异系统中的应用.     

带相关噪声异步采样系统的分布式最优线性递推融合估计

对带相关噪声的异步均匀采样线性离散系统, 研究了分布式最优线性递推融合预报和滤波问题. 通过引入 满足伯努利分布的随机变量将系统同步化, 给出了局部Kalman预报器和滤波器. 分别推导了局部估值间的互协方 差阵、分布式最优线性融合估值与局部估值间的互协方差阵. 提出了分布式最优线性递推融合预报器和滤波器. 与 局部估值按矩阵加权的分布式融合估计算法相比, 所提出的算法具有更高的估计精度, 但与集中式融合相比有精度 损失. 为了进一步提高估计精度, 又提出了带反馈的分布式最优线性递推融合预报器和滤波器, 证明了带反馈的融 合估计与集中式融合估计具有相同的精度. 仿真例子验证了所提算法的有效性.     

Optimal linear estimators for systems with multiple random measurement delays and packet dropouts

This article is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with possible multiple random measurement delays and packet dropouts, where the largest random delay is limited within a known bound and packet dropouts can be infinite. A new model is constructed to describe the phenomena of multiple random delays and packet dropouts by employing some random variables of Bernoulli distribution. By state augmentation, the system with random delays and packet dropouts is transferred to a system with random parameters. Based on the new model, the least mean square optimal linear estimators including filter, predictor and smoother are easily obtained via an innovation analysis approach. The estimators are recursively computed in terms of the solutions of a Riccati difference equation and a Lyapunov difference equation. A sufficient condition for the existence of the steady-state estimators is given. An example shows the effectiveness of the proposed algorithms.     

Centralized filtering and smoothing algorithms from outputs with random parameter matrices transmitted through uncertain communication channels

The least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the estimators and, due to random transmission failures, some of the data packet processed for the estimation may either contain only noise (uncertain observations), be delayed (sensor delays) or even be definitely lost (packet dropouts). Different sequences of Bernoulli random variables with known probabilities are employed to describe the multiple random transmission uncertainties of the different sensors. Using the last observation that successfully arrived when a packet is lost, the optimal linear centralized fusion estimators, including filter, multi-step predictors and fixed-point smoothers, are obtained via an innovation approach; this approach is a general and useful tool to find easily implementable recursive algorithms for the optimal linear estimators under the least-squares optimality criterion. The proposed algorithms are obtained without requiring the evolution model of the signal process, but using only the first and second-order moments of the processes involved in the measurement model.     

Optimal and suboptimal minimum-variance filtering in networked systems with mixed uncertainties of random sensor delays,packet dropouts and missing measurements

In this paper the Kalman filtering problem for networked stochastic linear discrete-time systems with random measurement delays, packet dropouts and missing measurements is studied. Based on a quasi Markov-chain approach, a unified/combined model is developed to accommodate random delay, packet dropout and missing measurement. Two approaches for constructing a filter via the linear matrix inequality approach are proposed. Simulation studies are then conducted to evaluate the effectiveness of the constructed estimators.     

Optimal full-Order filtering for discrete-time systems with random measurement delays and multiple packet dropouts

This paper is concerned with the estimation problem for discrete-time stochastic linear systems with possible single unit delay and multiple packet dropouts. Based on a proposed uncertain model in data transmission, an optimal full-order filter for the state of the system is presented, which is shown to be of the form of employing the received outputs at the current and last time instants. The solution to the optimal filter is given in terms of a Riccati difference equation governed by two binary random variables. The optimal filter is reduced to the standard Kalman filter when there are no random delays and packet dropouts. The steady-state filter is also investigated. A sufficient condition for the existence of the steady-state filter is given. The asymptotic stability of the optimal filter is analyzed.     

Optimal linear estimation for systems with multiple packet dropouts

This paper is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with multiple packet dropouts. Based on a packet dropout model, the optimal linear estimators including filter, predictor and smoother are developed via an innovation analysis approach. The estimators are computed recursively in terms of the solution of a Riccati difference equation of dimension equal to the order of the system state plus that of the measurement output. The steady-state estimators are also investigated. A sufficient condition for the convergence of the optimal linear estimators is given. Simulation results show the effectiveness of the proposed optimal linear estimators.     

基于群决策考虑属性效用一致性的DEA他评交叉效率公共权重排序法

针对一类主从式异构线性网络化多智能体系统,考虑每个智能体的反馈通道和前向通道中存在随机网络诱导时延和数据包丢失问题,采用预测控制方法,提出一种基于观测器的网络化多智能体协同输出跟踪控制方案.在该方案中,主智能体在每一时刻基于自身滞后输出和系统参考信号,计算一组控制预测序列和输出预测序列,前者用以主动补偿主智能体控制回路中的随机网络诱导时延和数据包丢失,后者被发往从智能体;从智能体在每一时刻基于主智能体发送过来的输出预测序列和自身滞后输出,计算一组控制预测序列,用以主动补偿从智能体控制回路中的随机网络诱导时延和数据包丢失;随后推导闭环网络化多智能体控制系统的稳定性,并通过实验验证该方案的有效性和可行性.     

具有传感器增益退化、传输时延和丢包的离线状态估计器

研究了具有传感器增益退化、数据传输时延和丢包的网络化状态估计问题,传感器增益退化现象通过统计特性已知的随机变量来描述,数据包时延和丢失发生于传感器量测输出向远程处理中心传送过程中,将各时延的发生描述为随机过程,在远程处理中心端建立只存储最新时刻数据包的时延-丢包模型,考虑到利用每一时刻实时的时延值和丢包情况,设计了一种离线的无偏估计器,推导出最小方差原则下的离线最优估计器增益.最后,通过算例仿真验证所设计离线状态估计器的有效性.