基于混合信号的状态估计方法

传统的系统状态估计方法只用到连续信号,而离散测量信号所包含的信息没有得到利用.提出一种基于混合信号(包括连续和离散)的系统状态估计方法,既利用了连续信号,也用到离散信号的信息.该方法将离散信号的变化视作系统的离散事件,提取其准确的信息并参与系统状态估计,构成具有混合系统特性的新型状态估计器.还讨论了该估计器的稳定性条件和设计方法.仿真实验证明这种所提出的状态估计方法可以有效地改善系统的状态估计性能.     

MIMO非线性系统状态估计的递推Monte-Carlo方法研究

递推蒙特·卡洛 (Monte-Carlo,MC)方法是基于系统状态和观测值概率分布的估计方法。本文首先讨论了SISO非线性系统状态估计中递推 MC方法的应用 ,在此基础上将上述状态估计方法推广到 MIMO非线性系统 ,并提出了两种不同的估计方案。仿真研究表明 ,这两种方案都可以得到较好的状态估计结果。不过 ,随机抽样数目对递推 MC估计方法的状态估计精度会产生较大影响 ,限制了该方法的进一步应用 ;本文对随机抽样数目对状态估计结果的影响进行了讨论。     

广义离散随机线性系统的降阶估计

该文讨论广义离散随机线性系统的状态估计问题，给出了一种降阶估计方法，该方法可直接估计系统的部分状态或系统状态的某个线性组合．     

不确定线性系统有限时间状态估计与故障重构

针对具有外部干扰和执行器故障的不确定线性系统,给出了一种有限时间内估计系统状态及重构执行器故障的方法．首先,通过状态和输出等价变换,得到不受执行器故障和建模不确定信息干扰的降维解耦系统．在此基础上设计有限时间状态估计器,并设置任意小的时延参数,实现对降维系统状态的有限时间估计,从而达到对原系统状态有限时间估计的目的;其次,考虑高增益滑模微分器对系统输出微分进行有限时间估计;之后,在原系统状态和系统输出微分有限时间估计的基础上,提出一种对系统不确定信息和执行器故障同时估计的方法;最后,通过对具有执行器故障的F-16飞行器纵向系统模型进行仿真,验证所提方法的有效性．     

一类基于状态估计的非线性系统的智能故障诊断

针对一类含有建模误差的非线性系统,研究了基于状态估计的智能故障诊断方法.首先提出一种状态估计器设计方法;然后在进行状态估计的同时用RBF神经网络来逼近系统所发生的故障.故障估计器的输入为系统的状态估计,所估计出的故障既可用作故障容错控制,也可用作报警.根据微分同胚,将含有建模误差的非线性系统变换为易于分析的规范形式,并在此基础上分析了故障诊断系统的稳定性和鲁棒性.仿真例子证明了该方法的有效性.     

多变量系统状态空间模型的递阶辨识

研究多变量系统状态空间模型的递阶辨识问题,推广了作者提出的标量系统状态和参数联合辨识算法.当状态可量测时,利用最小二乘原理直接辨识状态空间模型的参数矩阵;当状态不可测时,利用递阶辨识原理提出了状态空间模型递阶辨识方法,使用系统输入输出数据来估计系统的未知状态和参数.状态空间模型递阶辨识方法分为两步:首先假设系统状态是已知的(即参数估计算法中的未知系统状态用其估计代替),基于状态估计和系统输入输出数据递归计算系统参数估计;然后基于系统输入输出数据和获得的参数估计,递归计算系统的状态估计.     

混合系统二维离散与连续状态同步估计研究

在混合系统中,需要同时估计出系统的离散状态与连续状态.针对混合系统出现二维离散状态下的混合状态估计问题进行研究,根据系统特性,采用跳变马尔可夫线性系统建模,并应用Rao-Blackwellised粒子滤波算法对二维离散状态与连续状态进行同步估计.由于算法一定程度上缓解了粒子滤波算法在高维状态空间估计中的失效问题,并对离散状态单独采样,能提高系统状态的估计精度.仿真试验证明,方法能有效地同步估计出系统的二维离散状态与连续状态,其中,二维离散状态的估计准确率达到了96%.     

量子系统中状态估计方法的综述

从广泛用于实验量子领域的典型状态估计方法,到基于系统论观点、可用于量子反馈控制的状态估计方法,详细综述了4种测量方式下的相应量子状态估计方法及其适用背景.通过其发展历程的叙述,从本质上阐述了估计的基本原理,从技术上对各种方法进行了相应的分析和比较.同时,对量子状态估计和经典状态估计进行了相应的比较,并对量子系统中的状态估计方法作了总结.     

故障系统的状态估计

本文讨论故障系统的状态估计问题.文中给出了在故障检测滤波器误差方程基础上,利用未知输入观测器或逆系统算法,根据输出误差估计状态误差,从而得到故障系统状态的估计方法.文中还给出了一个数字仿真例子.     

通讯约束下的线性系统状态降维与估计

本文面向状态估计, 考察了通讯功率受限时线性动态系统状态的降维问题. 为了满足平行信道传输数据的维数限制和通讯功率约束, 采取降低状态维数的方法, 通过传输信号的新息, 提高传输效率, 利用有限的通信资源, 使得接收端的状态估计达到最优. 本文采用差分脉冲编码调制系统(DPCM), 基于最小误差熵估计准则和Kalman估计算法, 得出了最优的状态降维矩阵的设计方法, 并且对随机系统的可估计性以及对相应确定性系统的能观性进行了分析. 分析和仿真结果表明, 这种设计方法在传输信号满足通讯功率限制的条件下可以使接收端的状态估计性能达到最优.     

Stochastic sampling algorithms for state estimation of jump Markovlinear systems

Jump Markov linear systems are linear systems whose parameters evolve with time according to a finite-state Markov chain. Given a set of observations, our aim is to estimate the states of the finite-state Markov chain and the continuous (in space) states of the linear system. The computational cost in computing conditional mean or maximum a posteriori (MAP) state estimates of the Markov chain or the state of the jump Markov linear system grows exponentially in the number of observations. We present three globally convergent algorithms based on stochastic sampling methods for state estimation of jump Markov linear systems. The cost per iteration is linear in the data length. The first proposed algorithm is a data augmentation (DA) scheme that yields conditional mean state estimates. The second proposed scheme is a stochastic annealing (SA) version of DA that computes the joint MAP sequence estimate of the finite and continuous states. Finally, a Metropolis-Hastings DA scheme based on SA is designed to yield the MAP estimate of the finite-state Markov chain. Convergence results of the three above-mentioned stochastic algorithms are obtained. Computer simulations are carried out to evaluate the performances of the proposed algorithms. The problem of estimating a sparse signal developing from a neutron sensor based on a set of noisy data from a neutron sensor and the problem of narrow-band interference suppression in spread spectrum code-division multiple-access (CDMA) systems are considered     

Fault accommodation for linear systems with time-varying delay

This paper studies the fault accommodation problem for linear systems with time-varying delay, system uncertainties and external disturbances. A novel intermediate estimator-based fault tolerant control (FTC) scheme is proposed, where an intermediate estimator is constructed to estimate the states and the faults simultaneously, based on the estimation; an FTC compensation scheme is designed to eliminate the effects of faults, system uncertainties and time-varying delay. It is proved that the states of the closed-loop system are uniformly ultimately bounded. A simulation example shows the effectiveness of the proposed method.     

State estimation for networked control systems using fixed data rates

This paper investigates state estimation for linear time-invariant systems where sensors and controllers are geographically separated and connected via a bandwidth-limited and errorless communication channel with the fixed data rate. All plant states are quantised, coded and converted together into a codeword in our quantisation and coding scheme. We present necessary and sufficient conditions on the fixed data rate for observability of such systems, and further develop the data-rate theorem. It is shown in our results that there exists a quantisation and coding scheme to ensure observability of the system if the fixed data rate is larger than the lower bound given, which is less conservative than the one in the literature. Furthermore, we also examine the role that the disturbances have on the state estimation problem in the case with data-rate limitations. Illustrative examples are given to demonstrate the effectiveness of the proposed method.     

Water-quality estimation and control in streams

In this paper a methodology is given for the process of water-quality estimation and control in streams using a systems approach. The proposed procedure is based on the stream water quality model developed by Hassan et at. (1981). A linear observer is designed that can be used to estimate the unobservable states of the system. These states represent the concentration of the water-quality constituents in a given stream that are difficult, time-consuming or costly to measure. Although the system model is non-linear, the application of the developed linear observer in the case of the River Nile showed its stability and convergence to the actual states of the system. The output of this observer can also be used to derive the two-level hierarchical optimization scheme developed for this system in order to maintain water-quality standards in polluted reaches in a given water body with minimum cost.     

Consensus of linear multi‐agent systems with stochastic noises and binary‐valued communications

This article studies consensus problems of discrete‐time linear multi‐agent systems with stochastic noises and binary‐valued communications. Different from quantized consensus of first‐order systems with binary‐valued observations, the quantized consensus of linear multi‐agent systems requires that each agent observes its neighbors' states dynamically. Unlike the existing quantized consensus of linear multi‐agent systems, the information that each agent in this article gets from its neighbors is only binary‐valued. To estimate its neighbors' states dynamically by using the binary‐valued observations, we construct a two‐step estimation algorithm. Based on the estimates, a stochastic approximation‐based distributed control is proposed. The estimation and control are analyzed together in the closed‐loop system, since they are strongly coupled. Finally, it is proved that the estimates can converge to the true states in mean square sense and the states can achieve consensus at the same time by properly selecting the coefficient in the estimation algorithm. Moreover, the convergence rate of the estimation and the consensus speed are both given by O(1/t). The theoretical results are illustrated by simulations.     

Optimal linear mean square filter for continuous-time jump linear systems

We consider a class of hybrid systems which is modeled by continuous-time linear systems with Markovian jumps in the parameters (LSMJP). Our aim is to derive the best linear mean square estimator for such systems. The approach adopted here produces a filter which bears those desirable properties of the Kalman filter: A recursive scheme suitable for computer implementation which allows some offline computation that alleviates the computational burden. Apart from the intrinsic theoretical interest of the problem in its own right and the application-oriented motivation of getting more easily implementable filters, another compelling reason why the study here is pertinent has to do with the fact that the optimal nonlinear filter for our estimation problem is not computable via a finite computation (the filter is infinite dimensional). Our filter has dimension Nn, with n denoting the dimension of the state vector and N the number of states of the Markov chain.     

未知参数多变量线性系统自适应模糊广义预测控制

对未知参数多变量线性系统提出了自适应模糊广义预测控制方法.该方法直接用模糊逻辑系统组成的向量设计广义预测控制器,并基于广义误差向量估计值对控制器中的未知向量和广义误差估计值中的未知矩阵进行白适应调整.该方法不但能保证闭环系统所有信号有界,而且可使广义误差向量收敛到原点的一个邻域内.     

State estimation for coupled output discrete-time complex network with stochastic measurements and different inner coupling matrices

A state estimation problem is studied for a class of coupled outputs discrete-time networks with stochastic measurements, i.e., the measurements are missing and disturbed with stochastic noise. The considered networks are coupled with outputs rather than states, are coupled with different inner coupling matrices rather than identical inner ones. By using Lyapunov stability theory combined with stochastic analysis, a novel state estimation scheme is proposed to estimate the states of discrete-time complex networks through the available output measurements, where the measurements are stochastic missing and are disturbed with Brownian motions which are caused by data transmission among nodes due to communication unreliability. State estimation conditions are derived in terms of linear matrix inequalities (LMIs). A numerical example is provided to demonstrate the validity of the proposed scheme.     

Global output feedback control of nonlinear time-delay systems with input matching uncertainty and unknown output function

This paper studies the problem of global output feedback control for nonlinear time-delay systems with input matching uncertainty and the unknown output function, whose nonlinearities are bounded by lower triangular linear unmeasured states multiplying the unknown constant, polynomial-of-output and polynomial-of-input growth rates. By constructing a new extended state observer and skillfully combining the dynamic gain method, backstepping method and Lyapunov–Krasovskii theorem, a delay-independent output feedback controller can be developed with only one dynamic gain. It is proved that all the signals of the closed-loop system are bounded, the states of the original system and the corresponding observer converge to zero, and the estimation of input matching uncertainty converges to its actual value. Two examples demonstrate the effectiveness of the control scheme.