部分测量数据丢失的网络化系统的l2-l∞滤波

在网络系统中,滤波器得到的观测量通过有限带宽的通道进行传送可能发生丢失.本文研究一类具有测量数据部分丢失的网络化系统的l2-l∞滤波器设计问题.测量数据的丢失采用已知概率分布的二进制切换序列来描述.利用线性矩阵不等式方法,设计全阶和降阶滤波器.所设计的滤波器使得滤波误差系统均方指数稳定,而且保证相对于所有有界的外界扰动信号,滤波误差系统具有一定的l2-l∞扰动衰减水平.数字仿真表明设计方法的有效性.     

具有随机通讯时延的网络化系统的l2-l∞滤波

研究具有随机通讯时延的离散网络化系统的l2-l∞滤波器设计问题.采用满足Bernoulli分布随机变量描述测量数据的一步随机时延;利用线性矩阵不等式方法设计线性滤波器,使得滤波误差系统在均方意义下指数稳定,并保证对于所有有界的外界扰动信号,滤波误差系统具有一定的l2-l∞扰动衰减水平.数值仿真表明了所提出设计方法的有效性.     

时滞不确定离散系统的鲁棒l2-l∞滤波

将l2-l∞性能指标引入时滞不确定离散时间系统,研究基于这一指标的滤波器设计问 题.所研究的对象是同时具有状态时滞和多面体不确定性的离散时间系统.采用线性矩阵不等 式技术推导了此类不确定系统鲁棒l2-l∞滤波器存在的充分条件,并将滤波器的设计转化为一 个凸优化的求解问题,可以采用较为有效的内点方法进行求解.所设计的滤波器能够保证相对 于所有能量有界的外界扰动信号,滤波误差系统具有一定的l2-l∞扰动衰减水平.数值仿真验 证了所提出算法的可行性.     

随机时延离散系统的l2-l∞ 滤波器型迭代学习控制

对于具有随机时廷的一类离散系统的滤波器型迭代学习控制,采用满足Bernoulli分布的二进制序列来描述数据传输的随机时廷,利用线性矩阵不等式方法设计具有随机时廷的l2-l∞滤波器.给出了滤波误差系统均方指数稳定且具有给定的l2-l∞性能的充分条件,分析了滤波器型迭代学习控制收敛的充分条件.仿真实例说明了该设计方法的有效性.     

基于自适应事件触发的时滞系统分布式${l_2}text{-}{l_infty}$滤波

研究一类基于自适应事件触发机制的时滞系统分布式滤波问题.自适应事件触发条件由滤波器自身最新释放数据、当前时刻估计值及邻居节点最新释放数据共同决定.此事件触发机制采用阈值自适应调节方案,阈值参数在保证滤波器性能的前提下根据滤波误差动态变化,最大程度上节约网络通信资源.首先,给出滤波误差系统均方指数稳定的充分条件;其次,构造一个Lyapunov函数来分析滤波误差系统满足l2-l∞的性能指标;再次,设计离散时滞系统分布式l₂-l_∞滤波器,并通过线性矩阵不等式方法求解滤波器参数;最后,通过仿真实例说明滤波器能够降低系统时滞带来的影响,且在保证滤波性能前提下减少通信次数,节约网络资源.     

具有一步随机时滞和多丢包的网络系统H∞滤波器设计

在网络系统中由于连接传感器和滤波器的网络带宽有限,系统测量数据在传输中会出现随机时延甚至丢失. 本文讨论了具有一步随机时延和丢包的网络系统的H∞滤波器设计问题.基于新近提出的同时描述随机时延和丢包的模型,利用线性矩阵不等式方法设计线性滤波器,使得滤波误差系统均方指数稳定,并具有给定的H∞性能. 滤波器参数通过求解一个线性矩阵不等式得到.仿真研究说明了所提出算法的有效性.     

Delta算子系统具有圆形区域极点约束的非脆弱H∞滤波器设计: 一种LMI优化方法

研究了一类Delta算子线性系统具有圆形区域极点配置的非脆弱 H∞滤波器设计问题. 本文的目的是设计滤波器使得滤波误差系统不仅满足给定的圆点极点约束或D稳定约束, 而且从扰动输入到滤波误差的传递函数满足给定的 H∞范数约束. 所设计的滤波器具有乘性的滤波器增益变化, 基于Lyapunov稳定性理论和线性矩阵不等式方法, 给出具有区域极点配置的Delta算子离散系统非脆弱 H∞滤波器存在的充分条件. 数值仿真例子说明设计方法的有效性以及所提设计方法具有更小的保守性.     

随机时延网络化不确定系统的鲁棒H∞滤波

研究了一类具有随机时延的网络化不确定系统的 H∞ 滤波器设计问题. 这类时延的产生是由于传感器和滤波器通过有限带宽的网络连接而引起的系统测量数据的滞后, 而且它是随机发生的. 本文采用满足 Bernoulli 分布随机变量来描述测量数据的这种随机时延. 利用线性矩阵不等式, 给出了全阶和降阶的滤波器存在的充分条件. 所设计的滤波器使得滤波误差系统是均方指数稳定且具有给定的H∞ 性能. 数值仿真表明设计方法的有效性.     

离散时间不确定系统的混合l1/H∞滤波

研究离散时间不确定线性系统的混合l1/H∞滤波器设计问题,目的是找到一个稳定的线性滤波器,使滤波误差系统在不同的滤波通道内具有不同的性能指标.利用参数依赖Lyapunov函数法,推导出新的鲁棒l1/H∞性能准则.基于该性能准则推导了全阶和降阶鲁棒l1/H∞滤波器存在的充分条件,并将滤波器的设计问题转化为具有线性矩阵不等式约束的凸优化求解问题.     

具有测量数据丢失的网络化系统的故障检测滤波

研究一类具有测量数据部分丢失的网络化系统的故障检测滤波器设计问题. 测量数据的丢失假设满足已知概率的Bernoulli分布. 设计一个故障检测滤波器, 使残差误差系统均方指数稳定且具有一定的扰动衰减水平. 采用线性矩阵不等式方法, 给出了故障检测滤波器存在的充分条件. 最后通过例子说明该方法的有效性.     

增益随机变化随机丢包时滞系统非脆弱H∞滤波

鉴于目前的数字控制系统是离散的,设计了一类具有无穷分布时滞、随机丢失测量的离散系统的非脆弱H_∞滤波器．为了模拟离散系统增益随机变化现象,在滤波器的增益中引入高斯分布的随机变化分量;引入无穷分布时滞,刻画网络控制系统中有限的带宽产生的影响;进一步地,考虑由伯努利分布的白序列描述的随机丢失测量现象．基于李亚普诺夫稳定性理论、随机分析技术及线性矩阵不等式（LMI）技术,得到使整个滤波器增广系统渐近稳定,同时满足性能指标的非脆弱H_∞滤波器存在的充分条件,通过半定程序方法求解滤波器增益．最后,由数值仿真说明研究的有效性．     

测量丢失概率不确定的网络化系统的鲁棒故障检测

研究一类具有测量数据丢失概率不确定的网络化系统的鲁棒故障检测问题,系统用概率属于一个区间的Bernoulli随机二进制切换序列来描述测量数据丢失.设计一个基于观测器的鲁棒故障检测滤波器(RFDF),对于区间内的每一个概率,RFDF残差误差输出对故障敏感且对具有L2有界的未知干扰输入有一定的鲁棒性,应用线性矩阵不等式方法,给出并证明了此RFDF的存在条件.最后通过例子说明了该方法的有效性.     

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.     

H ∞ filtering for discrete-time singular networked systems with communication delays and data missing

In this article, the problem of H _∞ filter design is investigated for discrete-time singular networked systems with both multiple stochastic time-varying communication delays and probabilistic missing measurements. Two kinds of stochastic time-varying communication delays, namely stochastic discrete delays and stochastic distributed delays, are simultaneously considered. The purpose of the addressed filtering problem is to design a filter such that, for the admissible random measurement missing and communication delays, the filtering error dynamics is asymptotically stable in the mean square with a prescribed H _∞ performance index. In terms of linear matrix inequality (LMI) method, a sufficient condition is established that ensures the asymptotical stability in the mean square with a prescribed H _∞ performance index of the filtering error dynamics and then the filter parameters are characterised by the solution to an LMI. A numerical example is introduced to demonstrate the effectiveness of the proposed design procedures.     

Networked recursive filtering for time-delayed nonlinear stochastic systems with uniform quantisation under Round-Robin protocol

In this paper, the design of networked recursive filter is investigated for a kind of nonlinear stochastic systems subject to missing measurements, fixed time-delay and uniform quantisation under the Round-Robin protocol. In a digital platform, the information coming from multi-sensors could be subject to missing measurements due to the environment effect and then need to be transformed into digital signals via uniform quantizers. In addition, the Round-Robin protocol is adopted to govern the token accessing to channel media so as to both save energy and mitigate the data congestion. A novel extended Kalman-type recursive filter is constructed that firstly combines the Round-Robin protocol, namely, in the form of the augmentation of the plant system dynamics and the protocol-induced periodic measurements. In the simultaneous consideration of system delays, missing measurements, uniform quantisation, as well as the Round-Robin protocol, the purpose for the discussed filtering problem is to obtain a set of filter parameters over a finite-horizon to minimise the upper bound of filtering error covariance as far as possible. Via elaborate mathematical analysis, the desired filter parameter is obtained by virtue of solving two Riccati-type optimisation equations, which are dependent on the latest estimation states. The genetic algorithm has been introduced to optimise the dynamic parameter selections. In addition, it is revealed in theory that the trace of the upper bound of filtering error covariance is non-decreasing as the quantisation level increases. Finally, the effectiveness of the proposed design scheme is inspected by a discretised maneuvering target tracking system.     

Robust H ∞ filtering for networked systems with multiple state delays

In this paper, a new robust H _∞ filter design problem is studied for a class of networked systems with multiple state-delays. Two kinds of incomplete measurements, namely, measurements with random delays and measurements with stochastic missing phenomenon, are simultaneously considered. Such incomplete measurements are induced by the limited bandwidth of communication networks, and are modelled as a linear function of a certain set of indicator functions that depend on the same stochastic variable. Attention is focused on the analysis and design problems of a full-order robust H _∞ filter such that, for all admissible parameter uncertainties and all possible incomplete measurements, the filtering error dynamics is exponentially mean-square stable and a prescribed H _∞ attenuation level is guaranteed. Some recently reported methodologies, such as delay-dependent and parameter-dependent stability analysis approaches, are employed to obtain less conservative results. Sufficient conditions, which are dependent on the occurrence probability of both the random sensor delay and missing measurement, are established for the existence of the desired filters in terms of certain linear matrix inequalities (LMIs). When these LMIs are feasible, the explicit expression of the desired filter can also be characterized. Finally, numerical examples are given to illustrate the effectiveness and applicability of the proposed design method.     

具有测量数据部分丢失的离散系统的H∞滤波器设计

For packet-based transmission of data over a network, or temporary sensor failure, etc., data samples may be missing in the measured signals. This paper deals with the problem of H∞ filter design for linear discrete-time systems with missing measurements. The missing measurements will happen at any sample time, and the probability of the occurrence of missing data is assumed to be known. The main purpose is to obtain both full-and reduced-order filters such that the filter error systems are exponentially mean-square stable and guarantee a prescribed H∞ performance in terms of linear matrix inequality (LMI). A numerical example is provided to demonstrate the validity of the proposed design approach.     

Quantised recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements

This article is concerned with the recursive finite-horizon filtering problem for a class of nonlinear time-varying systems subject to multiplicative noises, missing measurements and quantisation effects. The missing measurements are modelled by a series of mutually independent random variables obeying Bernoulli distributions with possibly different occurrence probabilities. The quantisation phenomenon is described by using the logarithmic function and the multiplicative noises are considered to account for the stochastic disturbances on the system states. Attention is focused on the design of a recursive filter such that, for all multiplicative noises, missing measurements as well as quantisation effects, an upper bound for the filtering error covariance is guaranteed and such an upper bound is subsequently minimised by properly designing the filter parameters at each sampling instant. The desired filter parameters are obtained by solving two Riccati-like difference equations that are of a recursive form suitable for online applications. Finally, two simulation examples are exploited to demonstrate the effectiveness and applicability of the proposed filter design scheme.     

量测缺失下多速率传感器系统的H∞滤波器设计

提出了一类具有量测缺失和堆栈存贮特性的网络化多速率系统的H∞滤波问题. 采用提升技术将多速率系统转化为单速率系统, 并提出了一种解决由提升所带来的因果约束的方法, 然后通过构造李亚普诺夫稳定函数设计H∞滤波器. 在构造滤波器过程中, 发现采样速率越慢, 滤波设计的保守性越强. 算例表明了方法的有效性, 并且分析了不同采样周期以及不同的量测到达率对滤波器性能的影响.