Dissipative State Estimation Research Articles

Finite-Time Dissipative Fuzzy State Estimation for Jump Systems With Mixed Cyber Attacks: A Probabilistic Event-Triggered Approach.

This article addresses the finite-time dissipative fuzzy state estimation for Markov jump systems under mixed cyber attacks. A probabilistic event-triggered mechanism (PETM) is proposed to reduce the unwanted network traffic by using the statistic information of network-induced delays. The dual asynchronizations characterized by asynchronous modes and mismatched premise variables are tackled simultaneously. Under the PETM, Takagi-Sugeno (T-S) fuzzy state estimators are first constructed based on the imperfect measurements subject to mixed cyber attacks and exogenous disturbances. Less conservative criteria relying on both fuzzy rules and jumping modes are established to achieve the strictly (Q,S,R) - ϑ -dissipative finite-time state estimation performance. Furthermore, a synthesis algorithm is derived to calculate the fuzzy state estimator gains by virtue of an improved matrix decoupling technique. Finally, two examples are utilized to validate the effectiveness and advantage of the proposed results.

Nonfragile extended dissipativity state estimator design for discrete-time neural networks with time-varying delay

This paper studies the issue of nonfragile extended dissipativity state estimator design for discrete-time neural networks with time-varying delay. Firstly, a class of nonfragile proportional-integral state estimator with exponential type linear polynomial on time-varying delay is presented, which unifies the Arcak state estimator and the Luenberger state estimator as its special cases. Since the exponential type linear polynomial on time-varying delay is considered, more information of time delay can be utilized and the estimation error convergence rate can be adjusted. Secondly, an augmented Lyapunov–Krasovskii functional tailored for delayed discrete-time neural networks is presented, in which the double summation terms of the state vector and the information of the output estimation error are fully considered in the corresponding augmented vector, then a sufficient criterion that guarantees the nonfragile extended dissipativity state estimation for delayed discrete-time neural networks is derived. Furthermore, since an extended dissipativity performance level is introduced, the issues of passitivity state estimation, H∞ state estimation, L2-L∞ state estimation, and (Q,S,R)-γ dissipativity state estimation can be solved in a unified framework. Finally, simulation results are given to demonstrate the advantage of the presented method.

Dynamic Event-Triggered State Estimation for Markov Jump Neural Networks With Partially Unknown Probabilities.

This article focuses on the investigation of finite-time dissipative state estimation for Markov jump neural networks. First, in view of the subsistent phenomenon that the state estimator cannot capture the system modes synchronously, the hidden Markov model with partly unknown probabilities is introduced in this article to describe such asynchronization constraint. For the upper limit of network bandwidth and computing resources, a novel dynamic event-triggered transmission mechanism, whose threshold parameter is constructed as an adjustable diagonal matrix, is set between the estimator and the original system to avoid data collision and save energy. Then, with the assistance of Lyapunov techniques, an event-based asynchronous state estimator is designed to ensure that the resulting system is finite-time bounded with a prescribed dissipation performance index. Ultimately, the effectiveness of the proposed estimator design approach combining with a dynamic event-triggered transmission mechanism is demonstrated by a numerical example.

Unified dissipativity state estimation for delayed generalized impulsive neural networks with leakage delay effects

This paper examined for the first time the challenge of unified dissipativity state estimation of delayed generalized impulsive neural networks (GINNs) with leaky delay effects. To estimate the upper bounds of the Lyapunov Krasovskii functionals (LKFs), the tighter integral inequality (TII) and reciprocally convex inequality (RCI) approaches are employed, and certain criteria are provided in the form of linear matrix inequalities (LMIs). As a response, a new delayed-dependent criterion for establishing whether the estimated error network is unified dissipative is developed. By modifying the values of the suitable matrices, the notion of a unified dissipativity-based estimating state may be used to find the multi-dynamic state estimation of GINNs. The recommended technique’s benefit is displayed by multiple numerical examples, one of which was sponsored by a practical implementation of the benchmark problem (i.e., the quadruple-tank process system (QTPS)) that is involved with reasonable issues, particularly the impulse impacts in the sense of a unified dissipativity performance.

Generalized dissipative state estimation for discrete-time nonhomogeneous semi-Markov jump nonlinear systems

The problem of generalized dissipative state estimation for discrete-time nonhomogeneous semi-Markov jump nonlinear systems is concerned in this paper. In this paper, we consider the semi-Markov renewal chain is nonhomogeneous and the states of the system are inaccessible. The aim of this paper is to propose the estimator-designed method to ensure that the system is σ-mean-square stable and satisfy extended dissipative performance. By using the semi-Markov kernel method and polytopic approach, and constructing a new type of Lyapunov function, which not only depends on the sojourn-time but also on the stochastic switching rules, the state estimator gains can be obtained. At last, a numerical example is adopted to verify the superiority of the presented control strategy.

Event-Based Extended Dissipative State Estimation for Memristor-Based Markovian Neural Networks With Hybrid Time-Varying Delays

This paper aims to investigate the event-based extended dissipative state estimation problem for memristor-based Markovian neural networks in the presence of hybrid time-varying delays and sensor nonlinearity. To tackle the effect caused by information latching, sudden interference and environmental variation, the Markov jump model is employed to describe the memristor-based neural network. Besides, an event-triggered scheme is introduced to economize the cost of communication. Then some novel conditions are presented, which guarantee that the augmented error system is stochastically stable with an extended dissipative performance. The existence criterion of the desired mode-dependent estimator is also obtained in terms of linear matrix inequalities. Finally, simulation results are provided to show the effectiveness of the proposed method.

Non-fragile dissipative state estimation for semi-Markov jump inertial neural networks with reaction-diffusion

In this paper, the non-fragile dissipative state estimation is addressed for semi-Markov jump inertial neural networks with reaction-diffusion. A semi-Markov jump model is used to describe the stochastic jump parameters in networks. Different from the invariable transition probabilities in the traditional Markov jump systems, the transition probabilities of the semi-Markov jump systems rely on the stochastic sojourn-time. Accordingly, the Weibull distribution taking the place of the exponential distribution in this paper is adopted for the sojourn-time of each mode in the system. Firstly, by utilizing an applicable vector substitution, the second-order differential system could be converted into the first-order one. Afterwards, via constructing a seemly Lyapunov function of the semi-Markov inertial neural networks and adequately taking advantage of the peculiarities of cumulative distribution functions, some sufficient conditions with less conservatism are constructed to assure that the estimation error system is strictly (R1,R2,R3)−ϱ−dissipative stochastically stable. Based on these conditions, mode-dependent estimator gains are designed. Finally, a numerical example is proposed to validate the availability of the provided approach.

Extended dissipativity state estimation for generalized neural networks with time-varying delay via delay-product-type functionals and integral inequality

In this paper, the problem of extended dissipativity state estimation for delayed generalized neural networks (GNNs) is investigated. Firstly, in order to facilitate the use of more information of time-varying delay, a class of delay-product-type Lyapunov-Krasovskii functional (LKF) is proposed. Secondly, in order to accurately estimate the upper bound of the time-derivative of the constructed LKF, a delay-product-type integral inequality is proposed, then some sufficient conditions are obtained to guarantee the extended dissipativity state estimation for delayed GNNs. Moreover, the extended dissipativity state estimation can be used to tackle the problem of H∞ performance state estimation, passivity performance state estimation, L2-L∞ performance state estimation, and (Q,S,R)-γ-dissipativity state estimation for delayed GNNs. Finally, simulations are provided to illustrate the effectiveness of the proposed method.

Non-fragile distributed mode-dependent dissipative state estimation of uncertain Markov jump systems over sensor networks

This paper considers the non-fragile distributed state estimation problem for Markov jump systems over sensor networks based on dissipative theory. Moreover, both state estimator gain variations and parameter uncertainties are assumed to be with mode-dependent for more practical modeling. On the basis of stochastic analysis and Lyapunov–Krasovskii function method, sufficient conditions with desired mode-dependent estimators are established such that the prescribed dissipative performance can be achieved. In the end, the effectiveness and applicability of the developed scheme is confirmed via the illustrative example.

Non-fragile extended dissipative state estimation for delayed discrete-time neural networks: application to quadruple tank process model

This paper deals with the non-fragile state estimator design to study the robust extended dissipativity criterion for a class of discrete-time neural networks (DNNs) involving uncertainties as well as time-varying delay components. The requisite of the proposed problem is to design a proper state estimator such that the dynamics of the corresponding estimation error is extended dissipative having external disturbance and unpredictable fragility performance. By constructing an appropriate Lyapunov–Krasovskii functional (LKF), the sufficient conditions for the extended dissipativity performance of the error system obtained by means of the proposed DNNs and its estimator which includes the $$H_{\infty }$$ performance, $$L_{2}-L_{\infty }$$ performance, passivity and dissipativity performance in a unified framework. The established theoretical results are expressed in terms of linear matrix inequalities (LMIs) that can be easily checked by using the standard numerical softwares. In order to analyze the applicability and effectiveness of the proposed theoretical results, numerical examples including a quadruple tank process (QTP) system model have been illustrated with simulation results.

Extended dissipative state estimation for static neural networks via delay-product-type functional

This paper studies the issue of extended dissipative state estimation for static neural networks with time-varying delays. A new delay-product-type (DPT) functional is constructed to introduce triple integrals, which can encompass some existing DPT functionals as its special cases, which leads to less conservative results. A parameter-dependent reciprocally convex inequality (PDRCI) covering some existing results is proposed to estimate the DPT functional, which can reach a tighter bound. Based on these ingredients, a novel estimator design condition is obtained to ensure the estimation error system to be asymptotically stable and extended dissipative. By using a matrix inequality decoupling technique, the estimator gain matrices can be solved by linear matrix inequalities (LMIs). Compared with some existing works, the restrictions on slack matrices are overcome, which increase the flexibility of estimator solutions. The effectiveness of the developed method is illustrated by an example.

Dynamic Event-Based Non-Fragile Dissipative State Estimation for Quantized Complex Networks With Fading Measurements and Its Application

This article is concerned with the issue of dynamic event-based non-fragile dissipative state estimation for a type of stochastic complex networks (CNs) subject to a randomly varying coupling as well as fading measurements, where the variation of coupling is governed by a Markov chain. To characterize the measurement fading phenomenon for different nodes, a Rice fading model is considered with known statistics information of the coefficients. For the sake of further resource saving, a dynamic event-triggering strategy (ETS), which is proved to release less data packets than the static one, is implemented to govern the measurements transmission for each sensor to its corresponding estimator. The main objective of this article is to determine a dynamic event-based non-fragile estimator such that, for all possible parameter fluctuations in estimator gains, the estimation error system is stochastically stable with a strict ( $\Upsilon _{1}, \Upsilon _{2}, \Upsilon _{3}$ )- $\gamma $ -dissipativity. Through intensive stochastic analysis, sufficient conditions are then derived in terms LMI to guarantee the existence of the desired state estimator. Finally, the effectiveness of the proposed results are verified by two practical examples of Chua’s circuit and quadruple-tank process system (QTPS).

On estimating neuronal states of delayed neural networks based on canonical Bessel-Legendre inequalities

In this paper, the dissipativity state estimation problem is addressed for neural networks with time-varying delays. Applying the N-order Bessel-Legendre inequality, we formulate a hierarchical criterion on the existence of state estimators for the delayed neural networks. First, an augmented Lyaponov–Krasovskii functional is introduced to incorporate the vector information of the N-order Bessel-Legendre inequality. Second, a novel bounded real lemma (BRL) is established for the estimation error system. In particular, the quadratic delay coefficients are absorbed by introducing some new vectors, which, together with smaller upper bounds for certain integral items, leads to the BRL less conservative than some existing results. As such, the state estimators are designed via the linear matrix inequality approach. Third, the obtained criterion is proven to form a hierarchy, which means that the conservatism reduces with the increasing of the order N of the Bessel-Legendre inequality. Finally, the validity of our results is demonstrated through three examples.

Generalized Dissipativity State Estimation of Delayed Static Neural Networks Based on a Proportional-Integral Estimator With Exponential Gain Term

This brief investigates the problem of generalized dissipativity state estimation for static neural networks (SNNs) with time-varying delay. Firstly, a proportional-integral (PI) estimator with exponential gain term is proposed, which unifies the Luenberger estimator and the Arcak estimator based on generalized dissipativity. Secondly, an improved reciprocally convex inequality is proposed, which can be used to tackle the derivative of the Lyapunov functional. As a result, a new generalized dissipativity state estimation criterion can be derived and the gains of the designed estimator can be obtained. Finally, simulation results are provided to demonstrate the advantage and the effectiveness of the proposed method over the existing ones.

Generalized dissipativity state estimation for genetic regulatory networks with interval time-delay signals and leakage delays

This paper examines the state estimation issue of genetic regulatory networks (GRNs) including time delays and leakage delay term subject to unified dissipativity performance based on the Lyapunov functional approach. The main aim of the proposed state estimator is to access the true concentrations of the proteins and mRNAs, which is explored based on the available output measurements. As a first attempt, the generalized dissipativity concept is implemented for GRNs to generalizing the state estimation technique in the framework of linear matrix inequalities (LMIs) via an improved integral inequality together with the reciprocally convex inequality (RCI) technique. Then, the developed criterion ensures that the estimated error dynamics to be asymptotically stable and extended dissipative in addition to well monitored by the original dynamics via unified dissipativity performance. Finally, an interesting simulation example as a synthetic oscillatory network of transcriptional regulators in Escherichia coli is illustrated to explore the effectiveness and applicability of the developed state estimation technique, along with a comparison study also been exploited by using widely studied example.

Extended dissipative state estimation of delayed stochastic neural networks

In this article, extended dissipative state estimation (EDSE) criteria is established for stochastic neural networks with variable delay states. The EDSE criteria includes passivity state estimation, dissipativity state estimation, H∞ state estimation and L2−L∞ state estimation performances. Sufficient conditions for EDSE criteria are established by choosing Lyapunov–Krasovskii functionals (LKF) and using linear matrix inequality (LMIs) technique. New stochastic double integral inequality is applied for the integral term in the derivative of the chosen LKF in main results, which helps to establish EDSE criterion for delayed stochastic neural networks. The feasibility and superiority of the proposed approach is shown by providing numerical examples.

Dissipativity and exponential state estimation for quaternion-valued memristive neural networks

In this paper, the global dissipativity and exponential state estimation for quaternion-valued memristive neural networks are investigated. By starting from basic quaternion properties, several algebraic conditions ensuring the global dissipativity and state estimation problems are derived. It is noteworthy that, based on the vector ordering approach, one can compare the “magnitude” of two different quaternion-valued, and thus the closed convex hull derived by two different quaternion-valued connections can be derived. Simulation results are given to show the validate of the mathematical model and the derived conclusions of the memristive system.

Distributed Dissipative State Estimation for Markov Jump Genetic Regulatory Networks Subject to Round-Robin Scheduling.

The distributed dissipative state estimation issue of Markov jump genetic regulatory networks subject to round-robin scheduling is investigated in this paper. The system parameters randomly change in the light of a Markov chain. Each node in sensor networks communicates with its neighboring nodes in view of the prescribed network topology graph. The round-robin scheduling is employed to arrange the transmission order to lessen the likelihood of the occurrence of data collisions. The main goal of the work is to design a compatible distributed estimator to assure that the distributed error system is strictly (Λ 1,Λ 2,Λ 3) - γ -stochastically dissipative. By applying the Lyapunov stability theory and a modified matrix decoupling way, sufficient conditions are derived by solving some convex optimization problems. An illustrative example is given to verify the validity of the provided method.

Design of generalized dissipativity state estimator for static neural networks including state time delays and leakage delays

This paper discusses the issue of generalized dissipativity index for state estimation of static neural networks (SNNs) including leakage delays and state time delays. The integral terms in the time derivative of the Lyapunov–Krasovskii functionals (LKFs) are estimated by the well-known Wirtinger based integral inequality approach. As a result, a novel delay-dependent extended dissipativity state estimation condition is exploited based on the estimated error system is extended dissipative. The notion of the extended dissipativity based state estimation methodology is launched to investigate the L2−L∞ state estimation, H∞ state estimation, passivity state estimation, mixed H∞ and passivity state estimation, and (Q,S,R)−γ-dissipativity state estimation of SNNs by choosing free weighting matrices. Therefore, a new scheme is put forward to quantify multi-dynamic behaviors of SNNs in a combined structure by introducing of the free weighting matrices. The main scope of the addressed problem is to design state estimation criterion to estimate the neuron states such that, in the presence of both leakage delays and state time delays, the dynamics of the estimator error system is extended dissipative. In comparison with some recent results, much better and more dynamic behavior is performed by our methodology, which is immensely assisted from proposing a leakage delays and gain matrix in the system model. The advantage of the established methodology is explored by numerical examples and comparison results also are made along with the previous results.

Quantized asynchronous dissipative state estimation of jumping neural networks subject to occurring randomly sensor saturations

In this work, we study the quantized asynchronous dissipative state estimation issue for Markov jumping neural networks subject to randomly occurring sensor saturations. The network-induced phenomena (e.g. the sensor saturations and the signal quantization) are assumed to be occurring randomly. A stochastic Kronecker delta function is introduced to model such phenomena. The main work aims to design an asynchronous state estimator that assures the underlying error dynamics to be strictly (X,Y,Z)−γ−dissipative. Based on the stochastic Kronecker delta function method and stochastic analysis technique, we establish some conditions for the sake of guaranteeing the existence of the desired state estimator. We finally explain the effectiveness of the proposed design approach by means of a numerical example.