带Poisson跳非线性随机种群动力学模型的最优收获控制

研究了一类带跳的非线性随机群体动力学模型的最优收获控制.给出了在外界环境对系统产生影响的条件下带有Poisson跳的随机种群动力学系统;通过随机极大值原理,Hamilton函数及Ito公式,讨论了最优收获控制所满足的充分必要条件,所得到的结论是确定性种群系统的扩展.     

模型不确定非线性随机系统的鲁棒性能准则设计

对于一类模型不确定非线性随机系统,用耗散性的观点发展了鲁棒性能准则理论.特别地,将确定性非线性系统理论中的耗散性概念引入到模型不确定随机非线性系统中,并以此作为基础来发展H∞理论.在精确模型随机非线性系统H∞基础上,建立了模型不确定系统L2增益和HJI不等式的可解性的关系.由于HJI偏微分方程难于求解,考虑模型参数满足某种适当匹配条件的系统的鲁棒性能准则问题,我们不需要通过求解HJI方程就可以得到此类系统的H∞控制律.     

一类Itô型非线性时滞关联随机大系统的分散鲁棒控制

在非线性项满足全局Lipschitz条件下,本文研究了一类It(o)型非线性时滞关联随机大系统的分散鲁棒控制问题.系统的时滞是关于状态和控制输入的.基于Lyapunov泛函及线性矩阵不等式(LMI)的分析方法,得到了无记忆状态反馈控制器使整个时滞关联随机大系统可镇定的充分条件.     

一类It型非线性时滞关联随机大系统的分散鲁棒控制

在非线性项满足全局Lipschitz条件下,本文研究了一类It型非线性时滞关联随机大系统的分散鲁棒控制问题.系统的时滞是关于状态和控制输入的.基于Lyapunov泛函及线性矩阵不等式(LMI)的分析方法,得到了无记忆状态反馈控制器使整个时滞关联随机大系统可镇定的充分条件.     

一类非线性随机种群系统最优生育率控制

研究了一类非线性随机种群系统动力学模型的最优生育率控制问题.在加入外部随机因素扰动下,系统模型将会更具有实际意义.针对随机种群系统生育率控制问题,应用It?o公式,相应的伴随方程和变分不等式等经典理论,获得了随机种群系统最优生育率控制所满足的必要条件及其最优性组(由积分-偏微分方程和变分不等式组成).文中得到的结论是确定性种群系统的扩展,对随机控制理论具有现实的应用价值.     

一类It(o)型非线性时滞关联随机大系统的分散鲁棒控制

在非线性项满足全局Lipschitz条件下，本文研究了一类Ito型非线性时滞关联随机大系统的分散鲁棒控制问题．系统的时滞是关于状态和控制输入的．基于Lyapunov泛函及线性矩阵不等式（LMI）的分析方法，得到了无记忆状态反馈控制器使整个时滞关联随机大系统可镇定的充分条件．     

输出反馈下的滤波

本文给出带有线性反馈的线性随机系统的线性最小方差估计,和带有非线性反馈项的高斯随机系统的(非线性)最小方差估计的递推计算公式,继续[1]的工作,在较弱的条件下,证明了最小方差估计误差不依赖于控制,同时,对kalman滤波给出一个初等的证明方法。     

一类随机非线性系统的全局有限时间状态反馈镇定

研究一类连续但非光滑的随机非线性系统的全局有限时间状态反馈镇定控制问题.通过增加幂次积分器技术和四次Lyapunov函数的构造,系统地给出了系统状态反馈有限时间镇定控制器的设计方法.基于引入的随机非线性系统有限时间稳定的判定准则,可以证明闭环系统的有限时间稳定性.仿真结果进一步验证了所设计控制器的有效性.     

几种非线性隔离系统受随机激励时的响应分析

非线性隔离系统在现代隔振技术中是常用的.本文用Fokkef-Planck方程、统计线性化等方法研究了在随机激励下,硬非线性刚度类减振器的最佳阻尼选择;非反对称非线性刚度的单自由度隔离系统的响应特征;两自由度非线性隔离系统的响应分析.并通过计算实例,讨论了非线性隔离系统的一些参数选择.     

非线性系统双模鲁棒预测控制: 不变集切换方法

提出了一种基于不变集切换的非线性系统鲁棒预测控制算法.采用分段蕴含方法将非线性系统动态用一组线性变参数(LPV)系统动态包裹;计算出非线性系统的平衡面,对于每个LPV蕴含模型,针对相应的平衡点构造多面体不变集,得到覆盖非线性系统平衡面的一组相互重叠的不变集;在线根据系统当前状态所处的不变集和LPV区间切换控制律,最终保证闭环系统的稳定性.与传统的非线性预测控制相比,这种方法在构造不变集和确定控制律的计算都是离线进行,而在线只需根据当前状态切换控制律即可,从而避免了求解复杂的非凸非线性规划,在很大程度上降低了在线计算量.     

带有双噪声的随机SI传染病模型的稳定性与分岔

建立一个带有双噪声的随机SI传染病模型,运用随机平均法及非线性动力学理论对模型进行化简．通过Lyapunov指数和奇异边界理论,得到模型的局部随机稳定性和全局随机稳定性的条件．根据不变测度的Lyapunov指数和平稳概率密度,分析模型的随机分岔．结果表明,系统在随机因素作用下变得更敏感、更不稳定.     

Stochastic chaos in a Duffing oscillator and its control

Stochastic chaos discussed here means a kind of chaotic responses in a Duffing oscillator with bounded random parameters under harmonic excitations. A system with random parameters is usually called a stochastic system. The modifier ‘stochastic’ here implies dependent on some random parameter. As the system itself is stochastic, so is the response, even under harmonic excitations alone. In this paper stochastic chaos and its control are verified by the top Lyapunov exponent of the system. A non-feedback control strategy is adopted here by adding an adjustable noisy phase to the harmonic excitation, so that the control can be realized by adjusting the noise level. It is found that by this control strategy stochastic chaos can be tamed down to the small neighborhood of a periodic trajectory or an equilibrium state. In the analysis the stochastic Duffing oscillator is first transformed into an equivalent deterministic nonlinear system by the Gegenbauer polynomial approximation, so that the problem of controlling stochastic chaos can be reduced into the problem of controlling deterministic chaos in the equivalent system. Then the top Lyapunov exponent of the equivalent system is obtained by Wolf’s method to examine the chaotic behavior of the response. Numerical simulations show that the random phase control strategy is an effective way to control stochastic chaos.     

带跳的非线性随机微分方程的Lyapunov指数的估计

本文研究了带跳的非线性随机微分方程Lyapunov指数的估计,在适当的条件下,确定其Lyapunov指数q的值．对于给定的步长h,考虑此微分系统的Euler离散化模型,给出了的理论误差估计.     

Stochastic stability of Duffing–Mathieu system with delayed feedback control under white noise excitation

The asymptotic Lyapunov stability with probability one of Duffing–Mathieu system with time-delayed feedback control under white-noise parametric excitation is studied. First, the time-delayed feedback control force is expressed approximately in terms of the system state variables without time delay. Then, the averaged Itô stochastic differential equations for the system are derived by using the stochastic averaging method and the expression for the Lyapunov exponent of the linearized averaged Itô equations is derived. Finally, the effects of time delay in feedback control on the Lyapunov exponent and the stability of the system are analyzed. Meanwhile, the stability conditions for the system with different time delays are also obtained. The theoretical results are well verified through digital simulation.     

G-Brown运动驱动的非线性随机时滞微分方程的稳定化

研究了一类G-Brown运动驱动的非线性随机时滞微分方程的稳定化问题.首先,在一个不稳定的G-Brown运动驱动的非线性随机时滞微分方程的漂移项中设计了时滞反馈控制, 得其相应的控制系统.其次, 利用Lyapunov函数方法给出其相应的控制系统是渐近稳定的充分条件.最后, 通过例子说明了所得的结果.     

Adaptive Stabilization of Nonlinear Stochastic Systems

The purpose of this paper is to study the problem of asymptotic stabilization in probability of nonlinear stochastic differential systems with unknown parameters. With this aim, we introduce the concept of an adaptive control Lyapunov function for stochastic systems and we use the stochastic version of Artstein's theorem to design an adaptive stabilizer. In this framework the problem of adaptive stabilization of a nonlinear stochastic system is reduced to the problem of asymptotic stabilization in probability of a modified system. The design of an adaptive control Lyapunov function is illustrated by the example of adaptively quadratically stabilizable in probability stochastic differential systems. Accepted 9 December 1996     

Estimate on the Pathwise Lyapunov Exponent of Linear Stochastic Differential Equations with Constant Coefficients

In this article, we study the problem of estimating the pathwise Lyapunov exponent for linear stochastic systems with multiplicative noise and constant coefficients. We present a Lyapunov type matrix inequality that is closely related to this problem, and show under what conditions we can solve the matrix inequality. From this we can deduce an upper bound for the Lyapunov exponent. In the converse direction, it is shown that a necessary condition for the stochastic system to be pathwise asymptotically stable can be formulated in terms of controllability properties of the matrices involved.     

Stabilization of Passive Nonlinear Stochastic Differential Systems by Bounded Feedback

Abstract

The purpose of this paper is to give a systematic method for global asymptotic stabilization in probability of nonlinear control stochastic differential systems the unforced dynamics of which are Lyapunov stable in probability. The approach developed in this paper is based on the concept of passivity for nonaffine stochastic differential systems together with the theory of Lyapunov stability in probability for stochastic differential equations. In particular, we prove that, as in the case of affine in the control stochastic differential systems, a nonlinear stochastic differential system is asymptotically stabilizable in probability provided its unforced dynamics are Lyapunov stable in probability and some rank conditions involving the affine part of the system coefficients are satisfied. Furthermore, for such systems, we show how a stabilizing smooth state feedback law can be designed explicitly. As an application of our analysis, we construct a dynamic state feedback compensator for a class of nonaffine stochastic differential systems.     

Calculating the Lyapunov exponent for generalized linear systems with exponentially distributed elements of the transition matrix

A stochastic dynamic system of second order is considered. The system evolution is described by a dynamic equation with a stochastic transition matrix, which is linear in the idempotent algebra with operations of maximum and addition. It is assumed that some entries of the matrix are zero constants and all other entries are mutually independent and exponentially distributed. The problem considered is the computation of the Lyapunov exponent, which is defined as the average asymptotic rate of growth of the state vector of the system. The known results related to this problem are limited to systems whose matrices have zero off-diagonal entries. In the cases of matrices with a zero row, zero diagonal entries, or only one zero entry, the Lyapunov exponent is calculated using an approach which is based on constructing and analyzing a certain sequence of one-dimensional distribution functions. The value of the Lyapunov exponent is calculated as the average value of a random variable determined by the limiting distribution of this sequence.     

Research on nonlinear stochastic dynamical price model

In consideration of many uncertain factors existing in economic system, nonlinear stochastic dynamical price model which is subjected to Gaussian white noise excitation is proposed based on deterministic model. One-dimensional averaged Itô stochastic differential equation for the model is derived by using the stochastic averaging method, and applied to investigate the stability of the trivial solution and the first-passage failure of the stochastic price model. The stochastic price model and the methods presented in this paper are verified by numerical studies.