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.     

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.     

部分测量数据丢失的网络化系统的l-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.     

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.     

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.     

一类分布式时滞LPV系统的鲁棒H∞滤波

研究一类同时具有分布式和离散型时滞的线性参数变化系统的鲁棒H∞滤波问题．基于参数线性矩阵不等式方法，推导了滤波误差系统渐近稳定和具有H∞扰动衰减水平γ的时滞相关充分条件．同时应用投影定理，通过引入附加矩阵变量，解除了系统矩阵与依赖于参数的Lyapunov函数矩阵之间的耦合，使所得到的条件更适合于滤波器的综合．推导了系统鲁棒H∞滤波器存在的充分条件，并将滤波器的设计转化为一组线性矩阵不等式的求解．数值实例证明了所提出设计方案的可行性．     

一类变时滞饱和不确定系统鲁棒H∞滤波器设计

研究一类不确定时变时滞饱和系统的H∞滤波器设计问题.该类系统状态方程中含有时变时滞和有界不确定项,输出方程中含有饱和项,系统的噪声信号功率有界但统计特性未知.本文给出一种线性滤波器结构,提出一种新的设计方法.该方法主要应用Lyapunov-Krasovskii稳定性理论和线性矩阵不等式,来分析和设计滤波器;它能使得时变时滞项,饱和项以及不确定项在设计中得到有效处理,且所设计的滤波器能满足H∞性能指标,滤波器参数通过求解一种线性矩阵不等式来确定.例子的分析与仿真验证了本文方法的有效性.     

一类非线性时滞系统的H_∞鲁棒故障检测滤波器设计

研究一类受时变时滞影响的非线性不确定系统H∞鲁棒故障检测滤波器设计问题.首先采用基于观测器的故障检测滤波器作为残差产生器,将故障检测滤波器设计归结为H∞滤波问题;然后应用Lyapunov-Krasovskii方法,推导并证明了问题可解的依赖时滞的充分条件,通过求解线性矩阵不等式得到了观测器增益矩阵的解;最后通过算例验证了所提出方法的有效性.     

不确定离散时间系统的变增益H2H∞滤波

研究凸多面体不确定离散时间系统的变增益H2/H∞滤波器设计问题.通过引入附加松弛变量,提出一种新的Lyapunov矩阵与系统矩阵解耦的混合H2/H∞性能准则.基于该性能准则,推导了变增益H2/H∞滤波器存在的充分条件,并将滤波器设计问题转化为具有线性矩阵不等式约束的凸优化问题.仿真结果表明了所提出方法的有效性.     

随机网络控制系统的量化L2-L∞滤波

考虑到带宽有限网络环境下信号需经过量化处理才能进行发送,研究了一类带宽受限随机网络控制系统的L_2-L_∞滤波问题。采用对数量化器,将量化后的测量信号作为滤波器输入信号。首先将滤波误差系统建模成范数有界不确定随机时滞系统,进一步基于线性矩阵不等式方法推出了该随机网络控制系统的稳定性和滤波器设计的充分条件,并将滤波器的设计转化为一个凸优化的求解问题。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,随机网络控制系统的L_2-L_∞性能指标小于一定值γ。仿真实例证实了该设计方法的有效性。     

Robust Kalman filtering for uncertain discrete-time systems with probabilistic parameters bounded within a polytope

We consider the problem of finite horizon discrete-time Kalman filtering for systems with parametric uncertainties. Specifically, we consider unknown but deterministic uncertainties where the uncertain parameters are assumed to lie in a convex polyhedron with uniform probability density. The condition and a procedure for the construction of a suboptimal filter that minimizes an expected error covariance over-bound are derived.     

测量数据丢失的随机不确定离散系统的鲁棒H2状态估计

由于频宽有限,或者传感器临时损坏,测量数据在网络中传输时可能会丢失.本文对一类测量数据丢失的不确定离散系统,研究了鲁棒H2状态估计问题.所有的系统矩阵的参数都属丁二给定的凸多面体区域.测量数据的丢失是随机发生的,认为它是已知概率的Bernoulli随机序列.对于所有容许的不确定和可能的数据丢失,采用线性矩阵不等式方法,给出了全阶和降阶的H2滤波器存在的充分条件.数值仿真表明本文所提方法的有效性.     

Robust Kalman filtering for uncertain systems

This paper studies the problem of Kalman filter design for uncertain systems. The system under consideration is subjected to time-varying norm-bounded parameter uncertainties in both the state and measurement matrices. The problem we address is the design of a state estimator such that the covariance of the estimation error is guaranteed to be within a certain bound for all admissible uncertainties. A Riccati equation approach is proposed to solve the above problem. Furthermore, a suboptimal covariance upper bound can be computed by a convex optimization.     

Robust energy-to-peak filter design for stochastic time-delay systems

This paper considers the robust energy-to-peak filtering problem for uncertain stochastic time-delay systems. The stochastic uncertainties appear in both the dynamic and the measurement equations and the state delay is assumed to be time-varying. Attention is focused on the design of full-order and reduced-order filters guaranteeing a prescribed energy-to-peak performance for the filtering error system. Sufficient conditions are formulated in terms of linear matrix inequalities (LMIs), and the corresponding filter design is cast into a convex optimization problem which can be efficiently handled by using standard numerical algorithms. In addition, the results obtained are further extended to more general cases where the system matrices also contain uncertain parameters. The most frequently used ways of dealing with parameter uncertainties, including polytopic and norm-bounded characterizations, have been taken into consideration, with convex optimization problems obtained for the design of desired robust energy-to-peak filters.     

H∞ Estimation for Uncertain Systems With Limited Communication Capacity

This paper investigates the problem of robust H_infin estimation for uncertain systems subject to limited communication capacity. The parameter uncertainty belongs to a given convex polytope and the communication limitations include measurement quantization, signal transmission delay, and data packet dropout, which appear typically in a network environment. The problem of H_infin filter design is first solved for a nominal system subject to the aforementioned information limitations, which is then extended to the uncertain case based on the notion of quadratic stability. To further reduce the overdesign in the quadratic framework, this paper also proposes a parameter-dependent filter design procedure, which is much less conservative than the quadratic approach. The quadratic and parameter-dependent approaches provide alternatives for designing robust H_infin filters with different degrees of conservativeness and computational complexity. Two examples, including a mass-spring system, are utilized to illustrate the design procedures proposed in this paper.     

Robust filtering for a class of nonlinear stochastic systems with probability constraints

This paper is concerned with the probability-constrained filtering problem for a class of time-varying nonlinear stochastic systems with estimation error variance constraint. The stochastic nonlinearity considered is quite general that is capable of describing several well-studied stochastic nonlinear systems. The second-order statistics of the noise sequence are unknown but belong to certain known convex set. The purpose of this paper is to design a filter guaranteeing a minimized upper-bound on the estimation error variance. The existence condition for the desired filter is established, in terms of the feasibility of a set of difference Riccati-like equations, which can be solved forward in time. Then, under the probability constraints, a minimax estimation problem is proposed for determining the suboptimal filter structure that minimizes the worst-case performance on the estimation error variance with respect to the uncertain second-order statistics. Finally, a numerical example is presented to show the effectiveness and applicability of the proposed method.     

State estimation with probability constraints

This paper considers a state estimation problem for a discrete-time linear system driven by a Gaussian random process. The second order statistics of the input process and state initial condition are uncertain. However, the probability that the state and input satisfy linear constraints during the estimation interval is known. A minimax estimation problem is formulated to determine an estimator that minimises the worst-case mean square error criterion, over the uncertain second order statistics, subject to the probability constraints. It is shown that a solution to this constrained state estimation problem is given by a Kalman filter for appropriately chosen input and initial condition models. These models are obtained from a finite dimensional convex optimisation problem. The application of this estimator to an aircraft tracking problem quantifies the improvement in estimation accuracy obtained from the inclusion of probability constraints in the minimax formulation.     

A Reduced-order Fault Detection Filter Design for Polytopic Uncertain Continuous-time Markovian Jump Systems with Time-varying Delays

In this article, the fault detection (FD) reduced-order filtering problem is investigated for a family of polytopic uncertain continuous-time Markovian jump linear systems (MJLSs) with time-varying delays. Under meeting the the premise of fault detection accuracy, the reduced order fault detection filters are desirable in applications where fast data processing and saving computing space are necessary. Then, by the aid of the Markovian Lyapunov-Krasovskii functional and convex polyhedron techniques, some novel time-varying delays and polytopic uncertain sufficient conditions are proposed to insure the existence of the FD reduced-order filter. Finally, a simulated continuous stirred tank reactor process example is provided to verify the feasibility of the proposed methodology.     

Cooperative Containment Formation for Discrete-time Heterogeneous Networks by Double Compensators

In this paper, two practical distributed observers are constructed to solve the cooperative robust containment formation problem for discrete-time linear multi-agent systems. The dynamics of the systems contain uncertain parts. A containment error is presented to guarantee all the outputs of followers converge to the convex hull spanned by the outputs of the leaders. There are two compensators in this paper, we first present a distributed compensator to estimate the information of convex hull, and use an internal compensator to solve the problem of uncertain parts in dynamics. Further more, based on the both compensators, a distributed dynamic output feedback controller is designed to solve the containment control problem for the discrete-time multi-agent systems. Finally, a numerical example is given to verify the effectiveness of the main results.     

Fuzzy-model-based robust fault detection with stochastic mixed time delays and successive packet dropouts

This paper is concerned with the network-based robust fault detection problem for a class of uncertain discrete-time Takagi-Sugeno fuzzy systems with stochastic mixed time delays and successive packet dropouts. The mixed time delays comprise both the multiple discrete time delays and the infinite distributed delays. A sequence of stochastic variables is introduced to govern the random occurrences of the discrete time delays, distributed time delays, and successive packet dropouts, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fuzzy fault detection filter such that the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fuzzy fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. In addition, the optimal performance index for the addressed robust fuzzy fault detection problem is obtained by solving an auxiliary convex optimization problem. An illustrative example is provided to show the usefulness and effectiveness of the proposed design method.