随机控制系统Euler-Maruyama方法的均方指数输入状态稳定性

分析了随机控制系统Euler-Maruyama (EM)方法的均方指数输入状态稳定性. 本文的目的是寻找随机控制系统和EM方法分享它们均方指数输入状态稳定性的条件. 在全局Lipschitz系数和均方连续随机输入的基础上, 二阶矩的界和合适形式的强收敛条件被得到了. 在该强收敛条件下, 我们证明了一个随机控制系统是均方指数输入状态稳定的, 当且仅当对充分小的步长, EM方法也是均方指数输入状态稳定的.     

一类中立型随机延迟积分微分方程分裂步θ方法的均方指数稳定性

本文研究一类中立型随机延迟积分微分方程分裂步θ方法的均方指数稳定性.讨论了漂移项系数满足线性增长条件下精确解的均方指数稳定性.当θ∈[0,1/2]时,在漂移项满足线性增长和步长hh~*的条件下,分裂步θ方法可以保持精确解的均方指数稳定性;在θ∈(1/2,1]时该方法对任意步长h=τ/m保持原系统的均方指数稳定性.最后,数值试验验证了理论结果的正确性.     

衰减激励条件下最小均方算法的收敛性

给出了衰减激励信号的定义,并在衰减激励条件下,利用随机过程理论,研究了随机系统最小均方算法的收敛速率,阐述了参数估计误差收敛时,衰减指数和算法中设计参变量 (收敛因子或步长 )的选择方法.分析表明:在衰减激励条件下,最小均方算法也具有良好的性能:当衰减指数和设计参变量满足一定条件时,则参数估计误差一致收敛于零.     

网络攻击下电力系统的离散迟滞量化控制

针对随机网络系统受到攻击时的稳定问题，设计了一种基于离散时间观测状态和模态的迟滞量化反馈控制器，使得闭环随机系统均方指数稳定。离散观测减少了控制器接收到的信号并且保证信号主要内容不失真，从而提高控制器的效率，降低了网络通信负担。采用迟滞量化器有效地避免了对数量化器在量化过程中产生的抖震现象，并且考虑了当网络系统遭到网络欺骗攻击时，控制器能否保证系统的稳定性问题。给出均方指数稳定下的判据条件，根据稳定性判据条件设计基于离散状态和模态观测的迟滞量化反馈控制器，利用Lyapunov理论证明了随机网络非线性系统的均方指数稳定性，检验了判据条件的有效性。通过数值仿真验证了理论结果的有效性。     

随机延迟积分微分方程改进分步向后Euler方法的均方指数稳定性

本文研究一类改进分步向后Euler方法求解随机延迟积分微分方程的均方指数稳定性．证明了在约束网格下,该方法依步长h=τ／m保持原系统的均方指数稳定性．数值试验验证了本文理论结果的正确性．     

随机延迟微分方程分裂步单支θ方法的强收敛性

当扩散项系数g(x,y)关于变量x和y满足全局Lipschitz条件,而漂移项系数f(x,y)关于变量x满足单边Lipschitz条件,变量y满足全局Lipschitz条件时,本文建立了随机延迟微分方程分裂步单支θ方法的有界性和收敛性,并证明了当数值方法的参数θ满足1/2≤θ≤1时,分裂步单支θ方法对于这类随机延迟微分方程是强收敛的,并在现有文献的基础上将该方法从随机常微分方程推广到随机延迟微分方程.文末的数值试验验证了理论结果的正确性.     

随机时变时滞非线性马尔可夫跳跃系统故障检测问题研究

本文研究一类具有随机时变时滞及随机发生非线性(RONs)的离散时间马尔可夫跳跃系统的故障检测问题.针对多重时变时滞发生时所具有的随机特性,使用相互独立的伯努利随机过程来刻画这一现象.进一步地,在故障检测研究中考虑一类由伯努利分布白序列描述的随机非线性干扰.本文的目的是设计一个故障检测滤波器,使得整个误差动态系统满足均方指数稳定及相应的H∞性能指标.通过Lyapunov稳定性理论及随机分析技术,建立满足均方指数稳定和H∞干扰抑制的充分条件,进而使用半定程序方法求解一个凸优化问题,获取所设计的故障检测滤波器增益特性.最后,通过仿真算例证实了该设计方法的实用性和有效性.     

网络化时滞控制系统的有限时间稳定性分析

针对网络控制系统中存在于传感器-控制器-执行器间的双时延问题,提出了一种基于Markov模型的状态反馈控制策略.与传统应用Markov随机过程的方式相比,该策略采用两个Markov链描述每一个时延,通过状态反馈把该随机系统描述为具有四个随机参量的离散Markov跳变系统.利用Lyapunov有限时间稳定性理论分析得到该系统稳定的充分条件,并利用线性矩阵不等式(LMI)得到了可行的反馈矩阵.数值仿真结果进一步证明了该策略的有效性.     

求解随机常微分方程的平均单支θ-方法

本文针对随机常微分方程(Random ordinary differential equations)的路径近似提出了平均单支θ-方法.在单边Lipschitz条件下,得到该方法的路径收敛性,并研究了此类方法的B-稳定性,证明当θ∈[1/2,1],方法是B-稳定的.最后,数值实验验证了本文的结论.     

区间化时变时延的网络化切换系统建模与控制

研究了具有从传感器到控制器和控制器到执行器存在双边时变时延的网络控制系统指数稳定性的问题.首先将时延变化范围划分为多个等分区间,然后采用增广矩阵的方法建立了参数不确定的离散时间切换闭环系统模型.同时基于平均驻留时间分析方法,给出了系统满足指数稳定的条件,接着进一步的建立了时延区间划分个数与系统状态指数衰减率的定量关系.该方法有效降低了系统设计的保守性,一定程度上减少了系统状态收敛的时间.最后通过数值仿真验证了所提方法的有效性.     

Convergence and stability of numerical methods with variable step size for stochastic pantograph differential equations

The stochastic pantograph equations (SPEs) are very special stochastic delay differential equations (SDDEs) with unbounded memory. When the numerical methods with a constant step size are applied to the pantograph equations, the most difficult problem is the limited computer memory. In this paper, we construct methods with variable step size to solve SPEs. The analysis is motivated by the example of a mean-square stable linear SPE for which the Euler–Maruyama (EM) method with variable step size fails to reproduce this behaviour for any nonzero timestep. Then we consider the Backward Euler (BE) method with variable step size and develop the fundamental numerical analysis concerning its strong convergence and mean-square linear stability. It is proved that the numerical solutions produced by the BE method with variable step size converge to the exact solution under the local Lipschitz condition and the Bounded condition. Furthermore, the order of convergence p=½ is given under the Lipschitz condition. The result of the mean-square linear stability is given. Some illustrative numerical examples are presented to demonstrate the order of strong convergence and the mean-square linear stability of the BE method.     

Fast smooth second-order sliding mode control for stochastic systems with enumerable coloured noises

A fast smooth second-order sliding mode control is presented for a class of stochastic systems driven by enumerable Ornstein–Uhlenbeck coloured noises with time-varying coefficients. Instead of treating the noise as bounded disturbance, the stochastic control techniques are incorporated into the design of the control. The finite-time mean-square practical stability and finite-time mean-square practical reachability are first introduced. Then the prescribed sliding variable dynamic is presented. The sufficient condition guaranteeing its finite-time convergence is given and proved using stochastic Lyapunov-like techniques. The proposed sliding mode controller is applied to a second-order nonlinear stochastic system. Simulation results are given comparing with smooth second-order sliding mode control to validate the analysis.     

Stabilisation of stochastic coupled systems via feedback control based on discrete-time state observations

This paper is concerned with the stabilisation of stochastic coupled systems (SCSs) via feedback control based on discrete-time state observations. State feedback control based on discrete-time observations is designed in the drift parts of the SCSs. Based on graph theory and Lyapunov method, the upper bound of the duration between two consecutive state observations is obtained. And a systematic method is given to construct a global Lyapunov function for SCSs via feedback control based on discrete-time state observations. A Lyapunov-type theorem and a coefficient-type criterion are obtained to guarantee the stabilisation in the sense of mean-square asymptotical stability and mean-square exponential stability. Furthermore, we use the theoretical results to analyse the stabilisation of stochastic coupled oscillators. Finally, we give a numerical example to illustrate the effectiveness and feasibility of the developed theoretical results.     

Convergence and stability of the balanced methods for stochastic differential equations with jumps

This paper deals with the balanced methods which are implicit methods for stochastic differential equations with Poisson-driven jumps. It is shown that the balanced methods give a strong convergence rate of at least 1/2 and can preserve the linear mean-square stability with the sufficiently small stepsize. Weak variants are also considered and their mean-square stability analysed. Some numerical experiments are given to demonstrate the conclusions.     

Strong convergence of split-step theta methods for non-autonomous stochastic differential equations

In this paper, we first prove the strong convergence of the split-step theta methods for non-autonomous stochastic differential equations under a linear growth condition on the diffusion coefficient and a one-sided Lipschitz condition on the drift coefficient. Then, if the drift coefficient satisfies a polynomial growth condition, we further get the rate of convergence. Finally, the obtained results are supported by numerical experiments.     

Stability of stochastic θ-methods for stochastic delay integro-differential equations

In this paper, we are concerned with the numerical stability of linear stochastic delay integro-differential equations (SDIDEs). A sufficient condition for mean square stability of the exact solution of a linear SDIDE with multiplicative noise is derived. Then the mean square stability of stochastic θ-methods is investigated, and it is shown that the numerical solution can reproduce the mean square stability of the exact solution under appropriate conditions. At last, we present some numerical experiments to support our conclusions.     

On convergence of active disturbance rejection control for a class of uncertain stochastic nonlinear systems

In this paper, the practical mean-square convergence of active disturbance rejection control for a class of uncertain stochastic nonlinear systems modelled by the Itô-type stochastic differential equations with vast stochastic uncertainties is developed. We first design an extended state observer (ESO) to estimate both the unmeasured states and the stochastic total disturbance which includes unknown internal system dynamics, external stochastic disturbance without known statistical characteristics, unknown stochastic inverse dynamics, and uncertainty caused by the deviation of control parameter from its nominal value. The stochastic total disturbance is then cancelled (compensated) in the feedback loop. An ESO-based output-feedback control is finally designed analogously as for the system without uncertainties. The practical mean-square reference tracking and practical mean-square stability of the resulting closed-loop system are achieved. The numerical experiments are carried out to illustrate the effectiveness of the proposed approach.     

Approximate adjoint-based iterative learning control

This paper characterises stochastic convergence properties of adjoint-based (gradient-based) iterative learning control (ILC) applied to systems with load disturbances, when provided only with approximate gradient information and noisy measurements. Specifically, conditions are discussed under which the approximations will result in a scheme which converges to an optimal control input. Both the cases of time-invariant step sizes and cases of decreasing step sizes (as in stochastic approximation) are discussed. These theoretical results are supplemented with an application on a sequencing batch reactor for wastewater treatment plants, where approximate gradient information is available. It is found that for such case adjoint-based ILC outperforms inverse-based ILC and model-free P-type ILC, both in terms of convergence rate and measurement noise tolerance.