高斯白噪声激励下分数阶Duffing-Van der Pol系统的稳态响应

应用随机平均法研究了高斯白噪声激励下含有分数阶阻尼项的Duffing-Van der Pol系统的稳态响应.首先应用基于广义谐和函数的随机平均法得到系统关于幅值的平均伊藤微分方程并建立相应的平稳FPK方程,求解该平稳FPK方程的近似理论解得到系统幅值的稳态概率密度.分析幅值、位移和速度的稳态概率密度探究分数阶阻尼项以及其它参数对系统稳态响应的影响.发现降低分数阶的阶数可以增强系统的响应而增大分数阶的系数可以减弱系统响应.最后对原系统进行Monte Carlo数值模拟验证近似理论解的有效性.     

宽带随机激励下单自由度双边非对称碰撞振动系统的稳态响应

本文以一类单自由度双边非对称碰撞振动系统为研究对象,采用广义Hertz接触模型表示碰撞过程,考察系统在宽带随机激励下的稳态响应.应用基于广义谐和函数的随机平均法推导出系统在宽带随机外激励下的伊藤随机微分方程,通过求解相应的稳态FPK方程,得到系统关于幅值、能量和位移的稳态概率密度以及位移与速度的联合稳态概率密度.另外,将系统的随机响应近似为马尔可夫过程,利用广义胞映射法得到系统的近似稳态响应.最后通过与蒙特卡罗模拟结果的对比,验证了随机平均法和广义胞映射法的有效性.     

有界噪声激励下船舶横摇系统的响应分析

本文提出了一种改进的基于Jacobi椭圆函数的随机平均法,用于求解有界噪声激励下船舶横摇系统的随机响应问题.对谐波及有界噪声激励下的船舶横摇模型进行了局部灵敏度分析,研究了系统参数对船舶横摇角的影响,为之后随机分析奠定了基础.通过引入Jacobi椭圆函数,导出随机微分方程,应用随机平均法得到了关于横摇运动幅值和相位的伊藤方程,进一步求解相应的Fokker-Planck-Kolmogorov (FPK)方程,得到幅值概率密度.将该方法应用于有界噪声激励下船舶横摇系统,研究了系统的随机响应特性,通过与蒙特卡洛数值模拟结果比较验证了该方法的可行性与准确性.该方法所得的随机响应结果与灵敏度分析结果一致：激励幅值E,外激频率Ω,线性刚度系数γ对船舶横摇系统产生较大的影响,其余参数不影响船舶横摇运动的振幅,只对概率峰值的高度有影响.     

连续Markov跳变系统最优控制

针对连续Markov跳变系统,对其最优控制问题进行研究.首先,基于随机最大值原理,设计完全状态信息情形下Markov跳变系统的最优控制器;然后,采用导数原始定义结合Markov跳变系统特性的方法,得到了最优控制器系数矩阵的黎卡提微分方程,进而将其推广到不完全状态信息情形;最后,通过数值仿真验证了所得控制器的正确性.     

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.     

色噪声激励下一类驰振能量采集器的稳态响应研究

使用具有指数相关性的色噪声近似外界环境干扰,研究色噪声激励下驰振能量采集器(GEH)的稳态响应.首先,介绍了系统的理论机电耦合运动方程,并对其进行无量纲化处理.其次,通过广义谐波变换对方程进行等效解耦,然后使用能量包线随机平均法得到系统稳态响应的解析解.最后,使用四阶龙格库塔算法进行数值模拟,在验证解析解有效性的同时分析了风速、色噪声参数对系统稳态响应的影响.研究结果表明,较大的噪声强度有利于提升系统的能量采集性能,且在风速较低时效果更明显；噪声相关时间对系统稳态响应的影响与噪声强度相反.当系统的振动模式由风激励主导时,系统的响应表现为带有附加噪声的周期性振动；随着风速增加,增强的风激励削弱了色噪声激励对系统稳态响应的影响.此外,本文还研究了系统结构参数和电气参数对平均输出功率的影响,研究结果为优化系统设计提供了有效的理论指导.     

三稳态风致振动能量俘获系统分析

使用具有指数相关性的色噪声近似外界环境干扰,研究色噪声激励下驰振能量采集器(GEH)的稳态响应.首先,介绍了系统的理论机电耦合运动方程,并对其进行无量纲化处理.其次,通过广义谐波变换对方程进行等效解耦,然后使用能量包线随机平均法得到系统稳态响应的解析解.最后,使用四阶龙格库塔算法进行数值模拟,在验证解析解有效性的同时分析了风速、色噪声参数对系统稳态响应的影响.研究结果表明,较大的噪声强度有利于提升系统的能量采集性能,且在风速较低时效果更明显；噪声相关时间对系统稳态响应的影响与噪声强度相反.当系统的振动模式由风激励主导时,系统的响应表现为带有附加噪声的周期性振动；随着风速增加,增强的风激励削弱了色噪声激励对系统稳态响应的影响.此外,本文还研究了系统结构参数和电气参数对平均输出功率的影响,研究结果为优化系统设计提供了有效的理论指导.     

基于扩展状态观测器的随机隐Markov正跳变系统有限时间异步控制

针对一类含不匹配扰动的随机隐Markov跳变系统, 本文研究了基于扩展状态观测器(ESO)的有限时间异步 控制问题. 首先, 引入一组扩展变量将隐Markov跳变系统转换成一组新的随机扩展系统, 补偿不匹配扰动对系统控 制输出的影响. 基于Lyapunov–Krasovskii泛函方法, 给出使得基于ESO的闭环随机隐Markov增广跳变系统是正系 统, 且有限时间有界的充分条件. 进而得到直接求解观测器增益和控制器增益的线性矩阵不等式. 最后, 通过仿真结 果验证了本文所设计的异步状态反馈控制器和观测器的有效性和可行性.     

具有随机通信逻辑的网络控制系统稳定性分析

针对具有带宽约束的网络控制系统,讨论了在随机通信逻辑调度策略下系统的稳定性问题.将基于时倚泊松过程的随机通信逻辑引入系统中,建立了泊松强度与系统估计偏差之间的函数关系.利用随机点过程方法,证明了系统状态的更新时刻具有Markov特性,进而将系统转化为Markov跳变系统.利用随机稳定性理论,得到系统几乎处处稳定和均方渐近稳定的充分条件.仿真算例表明了该调度策略的有效性.     

计及随机传感器时滞的不确定半Markov跳变系统鲁棒滑模控制

在实际系统中,系统参数与结构随机变化、未知外界干扰、传感器时滞等现象时有发生并严重影响了系统的稳定运行.为了解决这一问题,本文提出计及随机传感器时滞的一类不确定半Markov跳变系统鲁棒滑模控制方法,其中系统的传感器时滞通过使用Bernoulli随机分布进行描述.考虑系统状态信息不可测量条件下,文章设计模态依赖Luenberger观测器去估计半Markov跳变系统的运行状态.然后,本文构造一个积分滑模面并借助随机Lyapunov理论,提出两种半Markov跳变系统的随机稳定性分析方法.进而,文章提出基于观测器的滑模控制方法使得系统状态能够在有限时间内到达滑模面上以及滑模动态在H_∞性能指标γ下是随机稳定的.最后,通过一种基于他励直流电动机模型的数值仿真例子验证所设计的滑模控制方法的有效性与正确性.     

网络控制系统随机稳定性研究

研究了具有随机网络诱导时延及数据包丢失的网络控制系统随机稳定性问题. 本文用一个具有两个状态的马尔可夫链来描述数据通过网络传输时随机数据包丢失过程, 利用马尔可夫跳变线性系统理论, 将网络控制系统建模为一个具有两种运行模式的马尔可夫跳变线性系统, 给出了在状态反馈控制下网络控制系统随机稳定的线性矩阵不等式形式的充分条件, 最后用一个仿真示例验证了该方法的有效性.     

Stabilizing nonlinear Markov jump systems with orthogonality between control and nonlinear terms

This article presents conditions to assure the second‐moment stability for a class of nonlinear Markov jump systems. The main assumption requires that the control and nonlinear terms form orthogonal vectors, a novel condition in the context of Markov jump systems. The second‐moment stability is verified through linear matrix inequalities—the conditions account the case in which the Markovian mode is either accessible or inaccessible to the controller. A real‐time automotive application illustrates the potential benefits of our approach.     

Stochastic stability of Ito differential equations with semi-Markovian jump parameters

In this note, the problem of stochastic stability for linear systems with jump parameters being semi-Markovian rather than full Markovian is further investigated. In particular, the system under consideration is described by Ito type nonlinear stochastic differential equations with phase type semi-Markovian jump parameters. Stochastic stability conditions are presented.     

不确定拟哈密顿系统的随机最优控制

本文提出了不确定拟哈密顿系统、基于随机平均法、随机极大值原理和随机微分对策理论的一种随机极大极小最优控制策略.首先,运用拟哈密顿系统的随机平均法,将系统状态从速度和位移的快变量形式转化为能量的慢变量形式,得到部分平均的It随机微分方程;其次,给定控制性能指标,对于不确定拟哈密顿系统的随机最优控制,根据随机微分对策理论,将其转化为一个极小极大控制问题;再根据随机极大值原理,建立关于系统与伴随过程的前向-后向随机微分方程,随机最优控制表达为哈密顿控制函数的极大极小条件,由此得到最坏情形下的扰动参数与极大极小最优控制;然后,将最坏扰动参数与最优控制代入部分平均的It随机微分方程并完成平均,求解与完全平均的It随机微分方程相应的Fokker-Planck-Kolmogorov(FPK)方程,可得受控系统的响应量并计算控制效果;最后,将上述不确定拟哈密顿系统的随机最优控制策略应用于一个两自由度非线性系统,通过数值结果说明该随机极大极小控制策略的控制效果.     

Mode-independent robust stabilization for uncertain Markovian jump nonlinear systems via fuzzy control.

This paper is concerned with the robust-stabilization problem of uncertain Markovian jump nonlinear systems (MJNSs) without mode observations via a fuzzy-control approach. The Takagi and Sugeno (T-S) fuzzy model is employed to represent a nonlinear system with norm-bounded parameter uncertainties and Markovian jump parameters. The aim is to design a mode-independent fuzzy controller such that the closed-loop Markovian jump fuzzy system (MJFS) is robustly stochastically stable. Based on a stochastic Lyapunov function, a robust-stabilization condition using a mode-independent fuzzy controller is derived for the uncertain MJFS in terms of linear matrix inequalities (LMIs). A new improved LMI formulation is used to alleviate the interrelation between the stochastic Lyapunov matrix and the system matrices containing controller variables in the derivation process. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method.     

Delay-range-dependent robust stabilisation and H ∞ control for nonlinear uncertain stochastic fuzzy systems with mode-dependent time delays and Markovian jump parameters

This article focuses on the problems of robust stabilisation and H _∞ control for nonlinear uncertain stochastic systems with mode-dependent time delay and Markovian jump parameters represented by the Takagi–Sugeno (T-S) fuzzy model approach. The system under consideration involves parameter uncertainties, Itô-type stochastic disturbances, Markovian jump parameters and unknown nonlinear disturbances. The purpose is to design a state feedback controller such that the closed-loop system is robustly exponentially stable in the mean square and satisfies a prescribed H _∞ performance level. Novel delay-range-dependent conditions in the form of linear matrix inequalities (LMIs) are derived for the solvability of robust stabilisation and H _∞ control problem. A desired fuzzy controller can be constructed by solving a set solutions of LMIs and can be easily calculated by Matlab LMI control toolbox. Finally, a numerical example is presented to illustrate the proposed method.     

Robust fuzzy control for uncertain discrete-time nonlinear Markovian jump systems without mode observations

This paper studies the robust fuzzy control problem of uncertain discrete-time nonlinear Markovian jump systems without mode observations. The Takagi and Sugeno (T-S) fuzzy model is employed to represent a discrete-time nonlinear system with norm-bounded parameter uncertainties and Markovian jump parameters. As a result, an uncertain Markovian jump fuzzy system (MJFS) is obtained. A stochastic fuzzy Lyapunov function (FLF) is employed to analyze the robust stability of the uncertain MJFS, which not only is dependent on the operation modes of the system, but also directly includes the membership functions. Then, based on this stochastic FLF and a non-parallel distributed compensation (non-PDC) scheme, a mode-independent state-feedback control design is developed to guarantee that the closed-loop MJFS is stochastically stable for all admissible parameter uncertainties. The proposed sufficient conditions for the robust stability and mode-independent robust stabilization are formulated as a set of coupled linear matrix inequalities (LMIs), which can be solved efficiently by using existing LMI optimization techniques. Finally, it is also demonstrated, via a simulation example, that the proposed design method is effective.     

Maximal and Stabilizing Hermitian Solutions for Discrete-Time Coupled Algebraic Riccati Equations

Discrete-time coupled algebraic Riccati equations that arise in quadratic optimal control and H _∞-control of Markovian jump linear systems are considered. First, the equations that arise from the quadratic optimal control problem are studied. The matrix cost is only assumed to be hermitian. Conditions for the existence of the maximal hermitian solution are derived in terms of the concept of mean square stabilizability and a convex set not being empty. A connection with convex optimization is established, leading to a numerical algorithm. A necessary and sufficient condition for the existence of a stabilizing solution (in the mean square sense) is derived. Sufficient conditions in terms of the usual observability and detectability tests for linear systems are also obtained. Finally, the coupled algebraic Riccati equations that arise from the H _∞-control of discrete-time Markovian jump linear systems are analyzed. An algorithm for deriving a stabilizing solution, if it exists, is obtained. These results generalize and unify several previous ones presented in the literature of discrete-time coupled Riccati equations of Markovian jump linear systems. Date received: November 14, 1996. Date revised: January 12, 1999.     

Improved H ∞ analysis of Markovian jumping stochastic systems with time-varying delays

This article investigates the problems of H _∞ analysis for Markovian jump stochastic systems with both nonlinear disturbance and time-varying delays. By virtue of the delay partition approach, the improved delay-dependent stochastic stability and bounded real lemma (BRL) for Markovian jump stochastic systems are obtained in terms of linear matrix inequalities (LMIs). The proposed approach involves neither free weighting matrices nor any model transformation, and it is shown that the new criteria have the capability of providing less conservative results than the state-of-the-art. Two numerical simulations are conducted to demonstrate the effectiveness of the proposed method in comparison with existing methods.