一类非均匀采样系统的最优状态滤波器

研究了一类非均匀采样离散随机系统的最优滤波问题,其中系统状态以快速率均匀进行更新,观测以慢速率非均匀进行采样,且状态更新率为观测采样率的整数倍.建立了在观测采样点上的非增广的状态模型.应用射影理论提出了在观测采样点上的线性最小方差最优状态滤波器.进而,基于观测采样点上的状态估值,提出了在状态更新点上的状态滤波器.最后,分析了所提出的滤波器的渐近稳定性和稳态性能.仿真研究验证了算法的有效性.     

通信受限下网络化系统的最优估计

研究了通信受限下网络化系统最优估计问题.由于通信受限,传感器节点无法同时将采样信息传输到远程滤波器.为此,文章提出了集中式最优估计算法和序贯式最优估计算法.前者将观测做扩维处理且具有较好的估计性能,但需要计算高维矩阵的逆,计算负担较大.后者无需计算高维矩阵的逆,具有实时性和灵活性,但损失了估计性能.这两种算法均可推广到测量丢失的情形.最后,通过一个目标跟踪的例子验证了所设计算法的有效性.     

具有内部控制和边界观测具有时滞的单管热交换方程的指数稳定性

本文研究了具有时滞边界观测和内部控制的单管热交换方程的指数稳定性.首先,将闭环系统转换为合适状态空间上的抽象柯西问题.通过验证,闭环系统生成一个一致有界的C_0半群,意味着系统存在唯一解.其次,分析了系统的谱分布,通过某些预解集上的预解式估计得到生成半群的最终可微性和最终紧性,这意味着系统的谱确定增长假设成立.最后,给出了系统指数稳定性的一个充分条件,此充分条件与物理参数有关而与时滞无关.     

具有丢失观测和网络传输滞后多通道系统的最优线性滤波

研究了具有丢失观测,一步观测滞后和随机丢包多通道系统的最优滤波器设计问题.首先通过三组Bernoulli分布随机变量来描述由传感器损耗造成的丢失观测以及网络数据传输过程中出现的一步滞后和多丢包现象.然后基于新息分析方法,提出了线性最小方差意义下的最优线性滤波器.它通过解一个Riccati方程和一个Lyapunov方程得到.最后给出了稳态最优滤波器存在的一个充分条件.仿真验证了其有效性.     

具时变时滞和 Markovian转换的不确定奇异随机系统的鲁棒 H_∞ 滤波(英文)

本文处理了一类具与模式有关的时变时滞和 Markovian转换的不确定奇异随机系统的鲁棒H∞滤波问题.所考虑的系统包含参数不确定性,Markovian参数,随机扰动和与模式有关的时变时滞.本文的目的是设计一个滤波器以保证滤波错误系统是正则的、无脉冲的、鲁棒指数均方稳定的和可达到一个给定的 H∞扰动衰减水平.文章首先得到所求鲁棒指数H∞滤波器存在的充分条件,然后给出所求滤波器参数的显示表示.     

基于观测器的一类模糊时滞系统的容错控制

针对一类带有传感器故障的模糊时滞系统,提出了一种实现对系统的状态和传感器故障估计的观测嚣设计方法.在此基础上,给出了模糊容错控制方法及保证模糊控制系统的渐近稳定充分条件.应用广义Lyapunov函数和线性矩阵不等式方法,证明了模糊闭环时滞系统的渐近稳定性.仿真结果进一步验证了所提出的方法和条件的有效性.     

一类时滞非完整系统的反馈控制问题的研究

针对一类具有时滞项的非完整系统,研究了其反馈控制器的设计问题.采用状态转换技术和反推方法,设计了不依赖于时滞的反馈控制器.同时为了处理初值为零的情况,提出了一种新颖的基于第一个子系统输出值的切换控制策略,最后通过仿真算例说明了控制器的有效性.     

具有泄漏时滞和混合加性时变时滞复数神经网络的状态估计

研究了具有泄漏时滞、加性离散时变时滞、加性分布时变时滞复数神经网络的状态估计问题.在复数神经网络不分解条件下, 通过构造合适的Lyapunov-Krasovskii泛函, 并应用自由权矩阵、矩阵不等式和倒数凸组合法等方法, 通过可观测的输出测量来估计神经元状态, 给出了判断误差状态模型全局渐近稳定的与时滞相关的复数线性矩阵不等式.最后, 通过一个数值仿真算例验证了理论分析的有效性.     

模糊广义系统的时滞依赖稳定与镇定条件

研究T-S模糊广义系统的时滞依赖稳定与镇定问题.利用Lyapunov泛函方法,得到一个线性矩阵不等式(LMIs)形式的时滞依赖稳定条件.本文所提方法考虑以前方法中通常忽略的有用的项,引入松弛变量矩阵和自由权重矩阵,估计Lyapunov泛函导数的上界;在此基础上,设计状态反馈模糊控制器,保证了闭环系统是局部正则、局部无脉冲和渐近稳定的.所得结果无需矩阵分解和利用锥补线性化方法进行迭代,最后通过两个仿真示例表明了本文结果具有较小的保守性.     

一类时滞非线性系统的自适应模糊控制

对于一类SISO输入时滞已知,状态时滞不确定但有上界的能采用后推设计方法的非线性系统提出一种基于后推设计、自适应模糊控制和滑模控制的控制方案.通过状态变换,把输入时滞系统转化为无输入时滞的系统.用模糊系统来估计系统的未知连续函数,对转化后的新系统设计自适应滑模控制器,使得新系统的状态有界,通过递推证得原系统的状态半全局一致有界.     

带时滞观测线性离散系统的递推滤波

本文讨论带时滞观测线性离散系统的滤波问题,提出利用联合分布函数,得到滤波递推格式.这种方法还适用于一般的带有状态时滞的线性离散系统.     

Delay-range-dependent robust H filtering for uncertain stochastic systems with mode-dependent time delays and Markovian jump parameters

This paper investigates the problem of robust H^∞ filtering for uncertain stochastic time-delay systems with Markovian jump parameters. Both the state dynamics and measurement of the system are corrupted by Wiener processes. The time delay varies in an interval and depends on the mode of operation. A Markovian jump linear filter is designed to guarantee robust exponential mean-square stability and a prescribed disturbance attenuation level of the resulting filter error system. A novel approach is employed in showing the robust exponential mean-square stability. The exponential decay rate can be directly estimated using matrices of the Lyapunov-Krasovskii functional and its derivative. A delay-range-dependent condition in the form of LMIs is derived for the solvability of this H^∞ filtering problem, and the desired filter can be constructed with solutions of the LMIs. An illustrative numerical example is provided to demonstrate the effectiveness of the proposed approach.     

Unscented filtering algorithm using two-step randomly delayed observations in nonlinear systems

An unscented filtering algorithm is derived for a class of nonlinear discrete-time stochastic systems using noisy observations which can be randomly delayed by one or two sample times. The update and the possible delays (of one and two sampling times) of any observation are modelled by using three Bernoulli random variables such that only one of them takes the value one. The algorithm performs in two-steps, prediction and update, and it uses a scaled unscented transformation to approximate the conditional mean and covariance of the state and observation at each time. The performance of the proposed filter is shown in a simulation example which uses a growth model with randomly delayed observations; in this example, the proposed filter is compared with the extended one obtained by linearizing the state and the observation equations and, also, with the unscented Kalman filter. A clear superiority of the proposed filter over the others is inferred.     

Optimal finite-dimensional solution for a class of nonlinear observation problems

The filtering problem in a differential system with linear dynamics and observations described by an implicit equation linear in the state is solved in finite-dimensional recursive form. The original problem is posed as a deterministic fixed-interval optimization problem (FIOP) on a finite time interval. No stochastic concepts are used. Via Pontryagin's principle, the FIOP is converted into a linear, two-point boundary-value problem. The boundary-value problem is separated by using a linear Riccati transformation into two initial-value problems which give the equations for the optimal filter and filter gain. The optimal filter is linear in the state, but nonlinear with respect to the observation. Stability of the filter is considered on the basis of a related properly linear system. Three filtering examples are given.     

Extended and unscented filtering algorithms using one-step randomly delayed observations

In this paper, the least squares filtering problem is investigated for a class of nonlinear discrete-time stochastic systems using observations with stochastic delays contaminated by additive white noise. The delay is considered to be random and modelled by a binary white noise with values of zero or one; these values indicate that the measurement arrives on time or that it is delayed by one sampling time. Using two different approximations of the first and second-order statistics of a nonlinear transformation of a random vector, we propose two filtering algorithms; the first is based on linear approximations of the system equations and the second on approximations using the scaled unscented transformation. These algorithms generalize the extended and unscented Kalman filters to the case in which the arrival of measurements can be one-step delayed and, hence, the measurement available to estimate the state may not be up-to-date. The accuracy of the different approximations is also analyzed and the performance of the proposed algorithms is compared in a numerical simulation example.     

Recursive state estimation for hybrid systems

The paper deals with recursive state estimation for hybrid systems. An unobservable state of such systems is changed both in a continuous and a discrete way. Fast and efficient online estimation of hybrid system state is desired in many application areas. The presented paper proposes to look at this problem via Bayesian filtering in the factorized (decomposed) form. General recursive solution is proposed as the probability density function, updated entry-wise. The paper summarizes general factorized filter specialized for (i) normal state-space models; (ii) multinomial state-space models with discrete observations; and (iii) hybrid systems. Illustrative experiments and comparison with one of the counterparts are provided.     

Particle Filters with Nudging in Multiscale Chaotic Systems: With Application to the Lorenz ’96 Atmospheric Model

This paper presents reduced-order nonlinear filtering schemes based on a theoretical framework that combines stochastic dimensional reduction and nonlinear filtering. Here, dimensional reduction is achieved for estimating the slow-scale process in a multiscale environment by constructing a filter using stochastic averaging results. The nonlinear filter is approximated numerically using the ensemble Kalman filter and particle filter. The particle filter is further adapted to the complexities of inherently chaotic signals. In particle filters, an ensemble of particles is used to represent the distribution of the state of the hidden signal. The ensemble is updated using observation data to obtain the best representation of the conditional density of the true state variables given observations. Particle methods suffer from the “curse of dimensionality,” an issue of particle degeneracy within a sample, which increases exponentially with system dimension. Hence, particle filtering in high dimensions can benefit from some form of dimensional reduction. A control is superimposed on particle dynamics to drive particles to locations most representative of observations, in other words, to construct a better prior density. The control is determined by solving a classical stochastic optimization problem and implemented in the particle filter using importance sampling techniques.

     

Finite-time filtering for switched linear systems with a mode-dependent average dwell time

This study considers the problem of finite-time filtering for switched linear systems with a mode-dependent average dwell time. By introducing a newly augmented Lyapunov–Krasovskii functional and considering the relationship between time-varying delays and their upper delay bounds, sufficient conditions are derived in terms of linear matrix inequalities such that the filtering error system is finite-time bounded and a prescribed noise attenuation level is guaranteed for all non-zero noises. Thus, a finite-time filter is designed for switched linear systems with a mode-dependent average dwell time. Finally, an example is given to illustrate the efficiency of the proposed methods.     

Self-triggered filter design for a class of nonlinear stochastic systems with Markovian jumping parameters

This paper is concerned with the self-triggered filtering problem for a class of Markovian jumping nonlinear stochastic systems. The event-triggered mechanism (ETM) is employed between the sensor and the filter to reduce unnecessary measurement transmission. Governed by the ETM, the measurement is transmitted to the filter as long as a predefined condition is satisfied. The purpose of the addressed problem is to synthesize a filter such that the dynamics of the filtering error is bounded in probability (BIP). A sufficient condition is first given to ensure the boundedness in probability of the filtering error dynamics, and the characterization of the desired filter gains is then realized by means of the feasibility of certain matrix inequalities. Furthermore, a self-triggered mechanism is designed to guarantee the filtering error dynamics to be BSP with excluded Zeno phenomenon. In the end, numerical simulation is carried out to illustrate the usefulness of the proposed self-triggered filtering algorithm.     

H∞ estimation for fuzzy membership function optimization

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a specific shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a few variables and the membership optimization problem can be reduced to a parameter optimization problem. The parameter optimization problem can then be formulated as a nonlinear filtering problem. In this paper we solve the nonlinear filtering problem using H_∞ state estimation theory. However, the membership functions that result from this approach are not (in general) sum normal. That is, the membership function values do not add up to one at each point in the domain. We therefore modify the H_∞ filter with the addition of state constraints so that the resulting membership functions are sum normal. Sum normality may be desirable not only for its intuitive appeal but also for computational reasons in the real time implementation of fuzzy logic systems. The methods proposed in this paper are illustrated on a fuzzy automotive cruise controller and compared to Kalman filtering based optimization.