一类乘性噪声随机系统的最优滤波算法

乘性噪声随机系统是一类含有乘性噪声因子的随机系统。对于乘性噪声随机系统的建模、滤波算法等问题的研究,还未展开。本文研究了一类离散时间乘性随机系统,基于卡尔曼滤波算法的思想,分别给出此类系统不含输入项及含有输入项的递推最优滤波算法,得到的结果便于实际应用。     

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.     

乘性随机离散系统的最优控制

基于对系统随机不确定因素的分析,文中定义了一种新型随机离散系统--乘性随机离散系统,并研究该类系统的线性二次型(LQ)最优控制问题.首先给出了该类系统的有限时间和无限时间LQ最优控制律,并着重分析、证明了无限时间LQ最优控制问题的Riccati方程的正定矩阵解的存在性及相应数值求解算法与收敛性,以及闭环系统的稳定性等问题.仿真结果表明了该方法的有效性.     

时滞线性随机系统的均方稳定性与反馈镇定*

本文研究Ｉｔｏｏ型随机滞后系统的均方稳定性与反馈镇定。文中首先建立了Ｉｔｏ型随机滞后系统的新型稳定性定理，然后采用适当的Ｌｙａｐｕｎｏｖ泛函得到了时滞线性随机系统零解均方渐近稳定的一个充分性判据，该判据适用于完全滞后型的随机系统，据此判据，文中给出了时滞线性随机系统的滞后反馈镇定方法。     

量测随机延迟下带相关乘性噪声的非线性系统分布式估计

本文提出了乘性噪声和加性噪声相关下的量测随机延迟非线性系统分布式状态估计.在所考虑系统中,相关状态被多传感器簇构成的传感器网所观测.所得理想量测被传送到远程分布式处理网,并伴随服从一阶马尔可夫过程的随机延迟.在此基础上,本文提出了分布式高斯信息滤波(distributed Gaussian-information filter,DGIF),来实现估计精度与计算时间的折中.在单处理节点/单元中,以估计误差协方差最小化为准则,设计了相应的高斯递推滤波,并实现了延迟概率的在线递推估计.进一步地,在分布式处理网中,基于非线性量测方程的统计线性回归,结合一致性算法,给出了一种分布式信息滤波形式,有效实现了分布式融合.分别在单处理单元和分布式处理网中仿真验证了所提算法的有效性.     

带多丢包和滞后随机不确定系统的最优线性估计

研究了带多丢包和滞后网络化随机不确定系统的最优线性估计问题. 通过白色乘性噪声来描述系统参数的随机不确定性. 通过一组满足Bernoulli分布的随机变量来描述数据传输过程中发生的丢包和滞后现象. 应用新息分析方法, 设计了线性最小方差意义下的最优线性估值器, 包括滤波器, 预报器和平滑器. 给出了稳态估值器存在的一个充分条件. 仿真例子验证了其有效性.     

具有加性和乘性噪声的线性离散时间随机系统的无模型最优跟踪控制

本文研究一类同时受加性和乘性噪声影响的离散时间随机系统的最优跟踪控制问题.通过构造由原始系统和参考轨迹组成的增广系统,将随机线性二次跟踪控制(SLQT)的成本函数转化为与增广状态相关的二次型函数,由此推导出用于求解SLQT的贝尔曼方程和增广随机代数黎卡提方程(SARE),而后进一步针对系统和参考轨迹动力学信息完全未知的情形,提出一种Q-学习算法来在线求解增广SARE,证明了该算法的收敛性,并采用批处理最小二乘法(BLS)解决该在线无模型控制算法的实现问题.通过对单相电压源UPS逆变器的仿真,验证了所提出控制方案的有效性.     

具有有色噪声的线性随机系统的矩稳定性

研究具有有色噪声的线性随机系统的矩稳定性,得到了无需计算矩阵方根的矩阵特征值代数判据,并用实例演示了文中的方法。     

带相关噪声、随机观测滞后和丢失的随机不确定系统的最优线性估值器

对带有限步相关噪声、乘性噪声、多步随机观测滞后和丢失的复杂网络化控制系统,根据相关噪声的步数,分析了噪声和状态、噪声和观测、噪声和新息、观测和新息、状态和新息之间的相关性,给出了相关阵的递推计算公式.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.一个网络监测环境下的三容器水箱系统的实例仿真,验证了算法的有效性.     

离散时间系统中随机谐振研究

摘要：基于一种新的信噪比的定义讨论了在四种经典噪声下在离散时间系统中的随机谐振现象。当输入信号是阈上信号时,噪声总是在恶化信号的传输,即随着噪声强度增加,输出信噪比呈现单调递减的趋势。然而当输入信号是阈下信号时。适当的噪声能够引起系统最优反应,即随着噪声强度的增加,输出信噪比达到一个最大峰值,也即存在明显的随机谐振现象。随着非线性系统中阈值的增加,随机谐振现象的明显度降低,当最佳噪声强度在增加时,输出信噪比的最优值却在减少。文中结果也说明,基于新的信噪比的定义是可以用来度量一个离散时间系统的随机谐振现象,且随机谐振对噪声具有一定的鲁棒性,拓广了随机谐振在信息传输领域的应用。     

Optimal control of discrete-time linear fractional-order systems with multiplicative noise

A finite horizon linear quadratic (LQ) optimal control problem is studied for a class of discrete-time linear fractional systems (LFSs) affected by multiplicative, independent random perturbations. Based on the dynamic programming technique, two methods are proposed for solving this problem. The first one seems to be new and uses a linear, expanded-state model of the LFS. The LQ optimal control problem reduces to a similar one for stochastic linear systems and the solution is obtained by solving Riccati equations. The second method appeals to the principle of optimality and provides an algorithm for the computation of the optimal control and cost by using directly the fractional system. As expected, in both cases, the optimal control is a linear function in the state and can be computed by a computer program. A numerical example and comparative simulations of the optimal trajectory prove the effectiveness of the two methods. Some other simulations are obtained for different values of the fractional order.     

带有乘性过程噪声和模式观测时滞离散马氏跳线性系统的状态估计

研究受乘性过程噪声干扰的离散马氏跳线性系统状态估计问题. 系统可得到的观测包括两部分: 模式观测和输出观测, 其中模式观测受到固定时滞的影响. 利用贝叶斯定理及所得到的一些结果, 提出一种新颖的最小均方误差意义下次优状态估计算法. 该次优算法是回归的, 并且不随着时间增加而加重计算存储负荷. 通过计算机仿真来评估所提出次优算法的性能, 仿真结果验证了该算法的优越性.

     

Optimal control with constrained total variance for Markov jump linear systems with multiplicative noises

We consider in this paper the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noises (MJLS-mn for short). Our objective is to present an optimal policy for the problem of maximising the system's total expected output over a finite-time horizon while restricting the weighted sum of its variance to a pre-specified upper-bound value. We obtain explicit conditions for the existence of an optimal control law for this problem as well as an algorithm for obtaining it, extending previous results in the literature. The paper is concluded by applying our results to a portfolio selection problem subject to regime switching.     

Asynchronous control of discrete-time stochastic bilinear systems with Markovian switchings

This paper is concerned with the problem of asynchronous control for a class of discrete-time Markov systems with multiplicative stochastic white noises. Based on a stability analysis scheme developed from mode-dependent Lyapunov function method, we first derive testable conditions in linear matrix inequality (LMI) setting to ensure the robust stability of the closed-loop system. We then recast the proposed stability conditions into equivalent forms that are later utilised to design a multi-mode asynchronous state-feedback controller (ASFC) that makes the closed-loop system stable. An extension to the case of deficient mode information (i.e. transition rates of the system and the controller are not fully accessible) is also presented. Finally, a model of networked control with DC devices is given to demonstrate the efficacy of the proposed design scheme.     

Quantized stabilization for stochastic discrete‐time systems with multiplicative noises

This paper considers the problem of quadratic mean‐square stabilization of a class of stochastic linear systems using quantized state feedback. Different from the previous works where the system is restricted to be deterministic, we focus on stochastic systems with multiplicative noises in both the system matrix and the control input. A static quantizer is used in the feedback channel. It is shown that the coarsest quantization density that permits stabilization of a stochastic system with multiplicative noises in the sense of quadratic mean‐square stability is achieved with the use of a logarithmic quantizer, and the coarsest quantization density is determined by an algebraic Riccati equation, which is also the solution to a special stochastic linear control problem. Our work is then extended to exponential quadratic mean‐square stabilization of the same class of stochastic systems. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal estimation of linear discrete-time systems with stochastic parameters

This paper considers optimal linear state estimation in the general case of linear discrete-time systems with stochastic parameters which are statistically independent with respect to time. The estimator is derived by transforming the system to one with deterministic parameters and state dependent additive system and observation noise. It is shown that mean square stability of the system is a sufficient and almost necessary condition for the existence, uniqueness and stability of the time invariant estimator.     

State estimation for discrete-time Markov jump linear systems with multiplicative noises and delayed mode measurements

In this paper, the state estimation problem for discrete-time Markov jump linear systems affected by multiplicative noises is considered. The available measurements for the system under consideration have two components: the first is the model measurement and the second is the output measurement, where the model measurement is affected by a fixed amount of delay. Using Bayes' rule and some results obtained in this paper, a novel suboptimal state estimation algorithm is proposed in the sense of minimum mean-square error under a lot of Gaussian hypotheses. The proposed algorithm is recursive and does not increase computational and storage load with time. Computer simulations are carried out to evaluate the performance of the proposed algorithm.     

Infinite horizon indefinite stochastic linear quadratic control for discrete-time systems

This paper discusses discrete-time stochastic linear quadratic (LQ) problem in the infinite horizon with state and control dependent noise, where the weighting matrices in the cost function are assumed to be indefinite. The problem gives rise to a generalized algebraic Riccati equation (GARE) that involves equality and inequality constraints. The well-posedness of the indefinite LQ problem is shown to be equivalent to the feasibility of a linear matrix inequality (LMI). Moreover, the existence of a stabilizing solution to the GARE is equivalent to the attainability of the LQ problem. All the optimal controls are obtained in terms of the solution to the GARE. Finally, we give an LMI -based approach to solve the GARE via a semidefinite programming.     

Containment control for distributed networks subject to multiplicative and additive noises with stochastic approximation–type protocols

This paper is concerned with the stochastic containment control problem for distributed network with multiplicative and additive noises, in which the stochastic approximation technique is utilized. A major challenge, compared with the existing results with multiplicative and additive noises, is that the involving leaders are dynamically changing. It is shown that finite boundedness for dynamical leaders is of great importance for addressing the stochastic containment control issue. A suitable approach is proposed to handle multiplicative and additive noises based on the stochastic approximation, and some necessary/sufficient conditions are derived for mean square convergence with mild requirements. Furthermore, the convergence rates are studied for both dynamical leaders and static leaders scenarios. We conclude this paper by using two illustrative examples to verify the effectiveness of the theoretical results.