Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

Finite-time H_(∞) filtering for Markov jump systems with uniform quantization

This paper is concerned with finite-time H_(∞) filtering for Markov jump systems with uniform quantization. The objective is to design quantized mode-dependent filters to ensure that the filtering error system is not only mean-square finite-time bounded but also has a prescribed finite-time H_(∞) performance. First, the case where the switching modes of the filter align with those of the MJS is considered. A numerically tractable filter design approach is proposed utilizing a mode-dependent Lyapunov function, Schur’s complement, and Dynkin’s formula. Then, the study is extended to a scenario where the switching modes of the filter can differ from those of the MJS. To address this situation, a mode-mismatched filter design approach is developed by leveraging a hidden Markov model to describe the asynchronous mode switching and the double expectation formula. Finally, a spring system model subject to a Markov chain is employed to validate the effectiveness of the quantized filter design approaches.

Noncooperative Model Predictive Game With Markov Jump Graph

In this paper,the distributed stochastic model predictive control(MPC)is proposed for the noncooperative game problem of the discrete-time multi-player systems(MPSs)with the undirected Markov jump graph.To reflect the reality,the state and input constraints have been considered along with the external disturbances.An iterative algorithm is designed such that model predictive noncooperative game could converge to the socalledε-Nash equilibrium in a distributed manner.Sufficient conditions are established to guarantee the convergence of the proposed algorithm.In addition,a set of easy-to-check conditions are provided to ensure the mean-square uniform bounded stability of the underlying MPSs.Finally,a numerical example on a group of spacecrafts is studied to verify the effectiveness of the proposed method.

H_(∞) state estimation for Markov jump neural networks with transition probabilities subject to the persistent dwell-time switching rule

We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-time switching rule,as a more general switching rule, is adopted to describe this variation characteristic. Afterwards, based on the classical Lyapunov stability theory, a Lyapunov function is established, in which the information about the Markov jump feature of the system mode and the persistent dwell-time switching of the transition probabilities is considered simultaneously.Furthermore, via using the stochastic analysis method and some advanced matrix transformation techniques, some sufficient conditions are obtained such that the estimation error system is mean-square exponentially stable with an H_(∞) performance level, from which the specific form of the estimator can be obtained. Finally, the rationality and effectiveness of the obtained results are verified by a numerical example.

Nonstationary Filtering for Markov Jumping Systems with Fading Channel and Multiplicative Noises

This paper studies the nonstationary filtering problem of Markov jump system under l2 - l∞ performance. Due to the difference in propagation channels, signal strength and phase will inevitably change randomly and cause the waste of signals resources. In response to this problem, a channel fading model with multiplicative noise is introduced. And then a nonstationary filter, which receives signals more efficiently is designed. Meanwhile Lyapunov function is constructed for error analysis. Finally, the gain matrix for filtering is obtained by solving the matrix inequality, and the results showed that the nonstationary filter converges to the stable point more quickly than the traditional asynchronous filter, the stability of the designed filter is verified.

Exponential stability for uncertain neutral systems with Markov jumps

This paper deals with the global exponential stability problems for stochastic neutral Markov jump systems (MJSs) with uncertain parameters and multiple time-delays. The delays are respectively considered as constant and time varying cases, and the uncertainties are assumed to be norm bounded. By selecting appropriate Lyapunov-Krasovskii functions, it gives the sufficient condition such that the uncertain neutral MJSs are globally exponentially stochastically stable for all admissible uncertainties. The stability criteria are formulated in the form of linear matrix inequalities (LMIs), which can be easily checked in practice. Finally, two numerical examples are exploited to illustrate the effectiveness of the developed techniques.

Robust reliable H_∞ control for discrete-time Markov jump linear systems with actuator failures

The robust reliable H∞ control problem for discrete-time Markovian jump systems with actuator failures is studied. A more practical model of actuator failures than outage is considered. Based on the state feedback method, the resulting closed-loop systems are reliable in that they remain robust stochastically stable and satisfy a certain level of H∞ disturbance attenuation not only when all actuators are operational, but also in case of some actuator failures. The solvability condition of controllers can be equivalent to a feasibility problem of coupled linear matrix inequalities (LMIs). A numerical example is also given to illustrate the design procedures and their effectiveness.

Controlling uncertain fuzzy neutral dynamic systems with Markov jumps

The robust H∞ control problems for stochastic fuzzy neutral Markov jump systems(MJSs) with parameters uncertainties and multiple time-delays are considered.The delays are respectively considered as constant and time varying,and the uncertain parameters are assumed to be norm bounded.By means of Takagi-Sugeno fuzzy models,the overall closed-loop fuzzy dynamics are constructed through selected membership functions.By selecting the appropriate Lyapunov-Krasovskii functions,the sufficient condition is given such that the uncertain fuzzy neutral MJSs are stochastically stability for all admissible uncertainties and satisfies the given H∞ control index.The stability and H∞ control criteria are formulated in the form of linear matrix inequalities,which can be easily checked in practice.Practical examples illustrate the effectiveness of the developed techniques.

Robust Finite-Time <i>H</i>_∞Filtering for Discrete-Time Markov Jump Stochastic Systems

This study is concerned with the problem of finite-time H∞ filter design for uncertain discrete-time Markov Jump stochastic systems. Our attention is focused on the design of mode-dependent H∞ filter to ensure the finite-time stability of the filtering error system and preserve a prescribed H∞ performance level for all admissible uncertainties. Sufficient conditions of filtering design for the system under consideration are developed and the corresponding filter parameters can be achieved in terms of linear matrix inequalities (LMI). Finally, a numerical example is provided to illustrate the validity of the proposed method.

Stochastic Stability of Discrete Time Positive Markov Jump Nonlinear Systems

This paper investigates the stochastic stability of discrete time positive Markov jump nonlinear systems(PMJNS).Some definitions on stochastic stability for discrete time PMJNS are introduced first.Then,using the multiply max-separable Lyapunov function method,some stochastic stability criterions of discrete time PMJNS are provided,and some corresponding criterions are also provided for discrete time positive Markov jump linear systems(PMJLS).Different from previous conclusions that require subsystems to be stable or marginally stable,the obtained results allow some subsystems to be unstable.Based on the proposed criterions,the stochastic stability behavior of discrete time positive Markov jump systems can be obtained just from the algebraic properties of the system function and the probability characteristics of the Markov chain.To illustrate the main results,two simulation examples are provided at the end.

Secure control for discrete-time hidden Markov jump systems subject to replay attacks via output feedback

This paper investigates the static output feedback secure control problem for discrete-time hidden Markov jump systems against replay attacks. The main purpose is to realise that closed-loopsystems are stochastically stable with or without replay attacks. Firstly, the tampered sensorsunder replay attacks can be identified via the proposed detection method. Then, an asynchronousstatic output feedback controller is designed, which can eliminate the negative impactcaused by replay attacks in view of the detection results. Based on the linear matrix inequalitytechnique, some sufficient conditions which ensure the closed-loop systems are stochasticallystable and meet a given H∞ performance are established. Finally, a numerical example and apractical example are given to verify the effectiveness and superiority of the proposed method.

非线性广义时滞Markov跳变系统的H_(∞)滑模控制

研究了一类转移速率部分未知的广义时滞Markov跳变系统的H_(∞)滑模控制问题.首先,对广义时滞Markov跳变系统设计了积分型滑模面,并求出对应的等效控制器.之后,构建Lyapunov泛函,通过线性矩阵不等式方法得到闭环系统随机容许且满足H_(∞)性能γ的充分条件,运用加减项方法以及引入自由权矩阵,使得到的结果降低了保守性;对滑模力学分析,设计了滑模控制器,使得系统能在有限时间内到达滑模面上.最后,通过数值算例与仿真验证了理论的可行性.

转移概率部分未知的离散时间Markov跳变系统Nash微分博弈

考虑到转移概率矩阵元素无法完全获悉,如何在转移概率部分未知的情境下研究离散时间Markov跳变系统Nash微分博弈是有待解决的问题之一,这一问题可以为转移概率部分未知的Markov跳变系统Nash微分博弈理论在管理问题上的应用提供理论支撑。基于此,本文首先研究单人博弈情形,即ε-次优控制问题,借助自由连接权矩阵和配方法,得到了ε-次优控制策略存在的充分性条件,并给出了成本函数上界的显式表达;然后延伸至双人博弈进行分析,得到了ε-次优Nash均衡策略存在的条件等价于求解双线性矩阵不等式和矩阵不等式的优化问题,并通过启发式算法求解优化问题得到ε-次优Nash均衡策略;最后通过数值算例证明了主要结论的有效性。

自适应事件触发机制下分布时滞Markov跳变网络化系统H_(∞)故障检测

传统事件触发机制中阈值的选择会对信号传输效率产生很大影响。针对此问题,研究了自适应事件触发机制下具有分布时滞的Markov网络化系统的故障检测问题。设计了一种模态相关的自适应事件触发机制,通过动态调整阈值,提高了网络资源利用率。考虑网络时延和数据丢包现象,同步设计触发器和故障检测滤波器。构造模态相关的Lyapunov泛数,根据积分不等式技术推导出使系统随机稳定且具有H_(∞)性能的充分条件。利用解耦方法和LMI技术求解出了故障检测滤波器参数。最后通过数值仿真验证了方案在节省网络资源上的优势。

T-S模糊Markov跳变系统的有限时间H_(∞)异步控制

针对一类在Takagi-Sugeno模糊规则下的Markov跳变系统,研究了基于模糊状态反馈控制器的有限时间H_(∞)异步控制问题。采用隐Markov模型刻画了模糊控制器与原系统的异步现象。首先,通过构造Lyapunov函数,运用有限时间有界性和H_(∞)控制理论,得到了闭环系统H_(∞)有限时间有界的充分条件。其次,设计了模糊状态反馈异步控制器,使得闭环系统H_(∞)有限时间有界。最后,通过仿真算例验证了方法的有效性和可行性。

事件触发机制下基于观测器的连续网络化Markov跳变系统的控制器设计

针对具有从传感器到执行器和执行器到控制器两段时延不确定的连续网络化Markov跳变系统,研究基于事件触发机制的观测器与控制器的协同设计问题.首先,考虑到系统状态不可测的情况,在控制器端构造观测器,通过对事件触发机制下信号传输时序的分析,将闭环系统建模为等价的时滞系统;其次,利用李雅普诺夫泛函稳定性定理和线性矩阵不等式方法,得到闭环系统稳定的充分条件;最后,给出控制器和观测器增益矩阵的求解方法,实现控制器和观测器的协同设计,并用数值仿真验证该设计方法的有效性.

A novel suboptimal algorithm for state estimation of Markov jump linear systems

This paper is concerned with state estimation problem for Markov jump linear systems where the disturbances involved in the systems equations and measurement equations are assumed to be Gaussian noise sequences.Based on two properties of conditional expectation,orthogonal projective theorem is applied to the state estimation problem of the considered systems so that a novel suboptimal algorithm is obtained.The novelty of the algorithm lies in using orthogonal projective theorem instead of Kalman filters to estimate the state.A numerical comparison of the algorithm with the interacting multiple model algorithm is given to illustrate the effectiveness of the proposed algorithm.

Stochastic Nash Games for Markov Jump Linear Systems with State-and Control-Dependent Noise

This paper investigates Nash games for a class of linear stochastic systems governed by Itô’s differential equation with Markovian jump parameters both in finite-time horizon and infinite-time horizon.First,stochastic Nash games are formulated by applying the results of indefinite stochastic linear quadratic(LQ)control problems.Second,in order to obtain Nash equilibrium strategies,crosscoupled stochastic Riccati differential(algebraic)equations(CSRDEs and CSRAEs)are derived.Moreover,in order to demonstrate the validity of the obtained results,stochastic H2/H∞control with state-and control-dependent noise is discussed as an immediate application.Finally,a numerical example is provided.

Markov Jump Processes in Estimating Sharing of Identity by Descent

Identity by descent(IBD)sharing is a very important genomic feature in population genetics which can be used to reconstruct recent demographic history.In this paper we provide a framework to estimate IBD sharing for a demographic model called two-population model with migration.We adopt the structured coalescent theory and use a continuous-time Markov jump process{X(t),t≥0}to describe the genealogical process in such model.Then we apply Kolmogorov backward equation to calculate the distribution of coalescence time and develop a formula for estimating the IBD sharing.The simulation studies show that our method to estimate IBD sharing for this demographic model is robust and accurate.

Asynchronous Dissipative Control and Robust Exponential Mean Square Stabilization for Uncertain Fuzzy Neutral Markov Jump Systems

This paper researches the strict dissipative control problem for uncertain fuzzy neutral Markov jump systems by Takagi-Sugeno fuzzy rules.The asynchronous phenomenon is considered between the uncertain fuzzy neutral Markov jump systems modes and asynchronous fuzzy P-D feedback controller modes,which is described by a hidden Markov model.Via using linear matrix inequalities,the desired asynchronous fuzzy P-D feedback controller is obtained,which can ensure that the closed-loop uncertain fuzzy neutral Markov jump systems satisfies robustly exponential mean square stabilization with strict dissipativity.A numerical example and a single-link robot arm are utilized to demonstrate the effectiveness of the method.