Non-fragile Estimation Research Articles

State Estimation for Measurement-Saturated Memristive Neural Networks with Missing Measurements and Mixed Time Delays Subject to Cyber-Attacks: A Non-Fragile Set-Membership Filtering Framework

This paper is concerned with the state estimation problem based on non-fragile set-membership filtering for a class of measurement-saturated memristive neural networks (MNNs) with unknown but bounded (UBB) noises, mixed time delays and missing measurements (MMs), subject to cyber-attacks under the framework of weighted try-once-discard protocol (WTOD protocol). Considering bandwidth-limited open networks, this paper proposes an improved set-membership filtering based on WTOD protocol to partially solve the problem that multiple sensor-related problems and multiple network-induced phenomena influence the state estimation performance of MNNs. Moreover, this paper also discusses the gain perturbations of the estimator and proposes an improved non-fragile estimation framework based on set-membership filtering, which enhances the robustness of the estimation approach. The proposed estimation framework can effectively estimate the state of MNNs with UBB noises, estimator gain perturbations, mixed time-delays, cyber-attacks, measurement saturations and MMs. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. The effectiveness of the theoretical results is verified by comparative numerical simulation examples.

Nonfragile state estimation for semi-Markovian switching CVNs with general uncertain transition rates: An event-triggered scheme

This paper tackles the problem of nonfragile state estimation for semi-Markovian switching complex-valued networks with time-varying delay. The concerned transition rates of the semi-Markov process are uncertain, including both the completely unknown ones and the inaccurately known ones with known bounds. To reduce the communication burden, a particular event-triggered generator is constructed, which depends on the latest available measurement output and a predefined positive threshold. Combining the stochastic analysis method with the Lyapunov stability theory, some less conservative criteria are obtained to ascertain the global asymptotic stability of the estimation error system in the mean-square sense. In addition, by solving some matrix inequalities, the desired nonfragile estimator gains are explicitly designed. Finally, a numerical example with simulations is given to illustrate effectiveness of the established estimation scheme.

Event-triggered non-fragile state estimator design for interval type-2 Takagi–Sugeno fuzzy systems with bounded disturbances

In this paper, the state estimator design problem of interval type-2 Takagi–Sugeno fuzzy systems suffering from bounded disturbances is studied. To enhance the resilience of the estimator, a non-fragile design scheme is proposed to tackle the estimator gain variations. Meanwhile, an event-triggered communication mechanism is introduced for relieving the transmission burden over networks. To settle down the non-fragile estimator design issue subject to bounded disturbances and event-induced error, we propose a new definition of quadratic boundedness via the multiple Lyapunov functions. Based on this definition, a novel co-design method of estimator and event generator for fuzzy system models in the presence of both measurable and immeasurable premise variables is presented. In virtue of quadratic boundedness framework, less conservative conditions of the existence and quadratic stability of the fuzzy estimators are obtained, and the upper bound of estimation error is given explicitly. The desired estimator gains are determined by convex optimization technique using slack matrices. Two illustrative examples are exploited to validate the availability and superiority of the addressed design approach.

Finite-Time Estimation for Markovian BAM Neural Networks With Asymmetrical Mode-Dependent Delays and Inconstant Measurements.

The issue of finite-time state estimation is studied for discrete-time Markovian bidirectional associative memory neural networks. The asymmetrical system mode-dependent (SMD) time-varying delays (TVDs) are considered, which means that the interval of TVDs is SMD. Because the sensors are inevitably influenced by the measurement environments and indirectly influenced by the system mode, a Markov chain, whose transition probability matrix is SMD, is used to describe the inconstant measurement. A nonfragile estimator is designed to improve the robustness of the estimator. The stochastically finite-time bounded stability is guaranteed under certain conditions. Finally, an example is used to clarify the effectiveness of the state estimation.

Reliable mixed [formula omitted] distributed estimation for periodic nonlinear systems with jumping topology

This paper investigates the problem of reliable mixed H2/H∞ distributed state estimation for periodic nonlinear systems. A sensor network with time-varying topology is used to measure the system, where the time-varying condition is described by a period index dependent Markov chain. A distributed state estimator is designed based on the local and neighbors’ innovation information of each node, while the non-fragile estimator is considered to improve the robustness of the estimator. An augmented estimation error system is derived, while the developed sufficient condition is carried out to ensure the stochastic stability and the mixed H2/H∞ performance. Then, the expected estimator gains are solved on the basis of the achieved sufficient condition. Finally, the effectiveness of the proposed state estimation method is emphasized by a numerical example and the comparative experiments.

Non-fragile l 2-l ∞ state estimation for time-delayed artificial neural networks: an adaptive event-triggered approach

In this paper, the state estimation problem is investigated for a kind of time-delayed artificial neural networks subject to gain perturbations under the adaptive event-triggering scheme. To avoid wasting resources, the event-triggering scheme is adopted during the data transmission process from the sensors to the estimator where the triggering threshold can be dynamically adjusted. By means of the Lyapunov stability theory, sufficient conditions are provided to ensure that the estimation error dynamics achieves both the asymptotical stability and the - performance. The desired non-fragile estimator gain is parameterised by solving certain matrix inequalities. At last, the usefulness of the proposed event-based non-fragile state estimator is shown via a numerical simulation example.

Dynamic event-based non-fragile state estimation for complex networks via partial nodes information

In this paper, the non-fragile state estimation problem is investigated for a class of continuous-time delayed complex networks. In the addressed complex network model, the outputs only from partial network nodes are used to fulfill the state estimation task. For improving the efficiency of resource utilization, a dynamic event-triggering mechanism is applied in the design of estimator, where an auxiliary time-varying parameter is introduced to dynamically modulate the triggering condition. Our intention is to obtain the gain parameters of the desired non-fragile state estimator, which can tolerate the norm-bounded gain perturbation. In virtue of a novel Lyapunov functional and matrix inequality technique, sufficient conditions are provided to ensure robustly exponential boundedness for estimation error dynamics, and gain matrices of the estimator are computed based on certain matrix inequalities. An illustrative simulation is presented to show the validity of the non-fragile estimator proposed.

Sampled-data non-fragile state estimation for delayed genetic regulatory networks under stochastically switching sampling periods

This paper is concerned with the non-fragile state estimation (NFSE) problem for a class of delayed genetic regulatory networks (GRNs) under stochastic sampling mechanisms. The measurement output is sampled before being transmitted to the estimator where the sampling period switches between two different values in a random way. By transforming the sampling periods into bounded time-delays, the issue of designing a non-fragile state estimator based on stochastically sampled measurements is converted into the NFSE problem for GRNs with multiple probabilistic interval delays. Then, by constructing a Lyapunov–Krasovskii functional and employing the Gronwall’s inequality together with Jensen’s inequality, a sufficient criterion is obtained to ensure the exponential mean-square stability of the corresponding estimation error dynamics. Furthermore, the desired non-fragile estimator gain is obtained via solving a convex optimization problem. Finally, the validity of the developed stochastic sampled-data NFSE scheme is verified via a numerical example.

Adaptive Event-Triggered Nonfragile State Estimation for Fractional-Order Complex Networked Systems With Cyber Attacks

This article addresses the adaptive event-triggered nonfragile state estimation for the fractional-order complex networked systems subject to randomly occurring nonlinearities and adversarial network attacks, in which the order of fractional derivative operator satisfies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0 &lt; p &lt; 1$ </tex-math></inline-formula> . To reduce unnecessary transmission burden as much as possible in an allowable range, an adaptive event-triggered scheme (AETS) is introduced to determine whether the data released by the sensor should be transmitted to the nonfragile state estimator. First, based on the considerations of the designed AETS and stochastic cyber-attacks, one constructs a newly fractional-order estimation error system model. Then, by employing the Lyapunov functional approach and the properties of Mittag–Leffler (M–L) functions, a sufficient condition is obtained to ensure the augmented error system stochastic mean-square stability; moreover, by making use of matrix’s singular value decomposition (SVD), the desired nonfragile state estimator is designed, and the estimator gains can be obtained by finding the feasible solutions of linear matrix inequality (LMI). Finally, a numerical example and Chua’s circuit model example are given to illustrate the feasibility of the designed nonfragile estimator.

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).

Non‐fragile set‐membership estimation for sensor‐saturated memristive neural networks via weighted try‐once‐discard protocol

This study is concerned with the non-fragile set-membership estimation problem for a class of delayed sensor-saturated memristive neural networks under the premise of communication protocol transmissions. The exogenous noises are unknown but bounded and the activation function satisfies the sector-bounded condition. In order to schedule the limited network resources, the node's utilisation right of the communication channel at the current instant is determined by the weighted try-once-discard protocol. Besides, the estimator gain perturbations are considered to enhance the robustness of estimation method. The major focus of the study is to a design a non-fragile estimator such that, for all measurement saturation, mixed time-delays and estimator gain perturbation, the estimation error exists in the ellipsoid by providing a sufficient criterion and the optimal semi-axes length of the ellipsoid is found by solving a convex optimisation problem. Finally, a simulation example shows that the proposed non-fragile estimation strategy is effective.

Non-fragile estimation for discrete-time T--S fuzzy systems with event-triggered protocol

This paper investigates the non-fragile state estimation problem for a class of discrete-time T-S fuzzy systems with time-delays and multiple missing measurements under event-triggered mechanism. First of all, the plant is subject to the time-varying delays and the stochastic disturbances. Next, a random white sequence, the element of which obeys a general probabilistic distribution defined on $[0,1]$, is utilized to formulate the occurrence of the missing measurements. Also, an event generator function is employed to regulate the transmission of data to save the precious energy. Then, a non-fragile state estimator is constructed to reflect the randomly occurring gain variations in the implementing process. By means of the Lyapunov-Krasovskii functional, the desired sufficient conditions are obtained such that the Takagi-Sugeno (T-S) fuzzy estimation error system is exponentially ultimately bounded in the mean square. And then the upper bound is minimized via the robust optimization technique and the estimator gain matrices can be calculated. Finally, a simulation example is utilized to demonstrate the effectiveness of the state estimation scheme proposed in this paper.

Further improved results on non-fragile H∞ performance state estimation for delayed static neural networks

This work investigates the non-fragile H∞ state estimation issue for static neural networks (SNNs) with mixed time-varing delays and randomly occurring uncertainties (ROUs). ROUs with Bernoulli distributed white noise sequences are firstly considered for tackling state estimation of SNNs, which are mutually uncorrelated stochastic variables. In order to take full advantage of the slope information about activation function (SIAAF), the estimation error of activation function is separated into two parts. Based on the more SIAAF, a modified Lyapunov-Krasovskii functional (LKF) is constructed. In addition, by combining integral inequality and zero equality with several parameters, further improved results are emerged to ensure the error system is globally asymptotically stable with a prescribed level γ and reduce conservativeness to some extent. Furthermore, the more practical non-fragile estimator gain matrix can be obtained via the above designed optimization algorithm. Finally, two numerical examples are furnished to verify the effectiveness and performance of the developed method.

Trajectory Tracking With Constrained Sensors and Unreliable Communication Networks

This work investigates the remote vehicle tracking issue over constrained monitoring sensors and unreliable communication networks. A saturation function is used to describe the bounded time varying acceleration of the vehicle. A set of matrices are introduced to model the sensor monitoring conditions called captured states (CSs), and a Markov chain with time varying and partially unknown transition probability (TVPUTP) is proposed to analyze the conditions of the CSs. Then, a CS dependent nonfragile estimator is designed based on the measured unreliable vehicle information, and the estimation error system (EES) is derived. Two theorems are established to ensure that the EES satisfies the finite-horizon (FH) $H_\infty $ performance. Finally, an example is introduced to show the effectiveness of the results.

Non-fragile state estimation for delayed fractional-order memristive neural networks

The issue of non-fragile estimation for fractional-order memristive system is provided in this paper. By endowing the Lyapunov technique, the corresponding works that ensuring the globally asymptotic stability of the error model are presented, which can be calculated efficiently. In the end, the analytical methods are voiced by two simulations.

Finite-time state estimation for delayed periodic neural networks over multiple-packet transmission

Abstract In this paper, the problem of finite-time state estimation for delayed periodic neural networks over multiple-packet transmission is addressed. The components of measurement output are separately transmitted by multiple-packet transmission, and the randomly occurring packet dropouts of different channels are described by mutually independent Bernoulli processes. In order to improve the robustness of the estimator, a non-fragile estimator is designed. In addition, some sufficient criteria are given to ensure that the estimation error system is stochastically finite-time stable and stochastically finite-time bounded, and the gains of non-fragile estimator are then derived based on these results. Finally, simulation results are provided to illustrate the effectiveness of the proposed estimator design approach.

Nonfragile asynchronous control for uncertain chaotic Lurie network systems with Bernoulli stochastic process

SummaryThis paper proposes a novel nonfragile robust asynchronous control scheme for master‐slave uncertain chaotic Lurie network systems with randomly occurring time‐varying parameter uncertainties and controller gain fluctuation. The asynchronous phenomenon occurs between the system modes and the controller modes. In order to consider a more realistic situation in designing a reliable proportional‐derivative controller, Bernoulli stochastic process and memory feedback are introduced to the concept of nonlinear control system. First, by taking full advantage of the additional derivative state term and variable multiple integral terms, a newly augmented Lyapunov‐Krasovskii functional is constructed via an adjustable parameter. Second, based on new integral inequalities including almost all of the existing integral inequalities, which can produce more accurate bounds with more orthogonal polynomials considered, less conservative synchronization criteria are obtained. Third, a desired nonfragile estimator controller is achieved under the aforementioned methods. Finally, 4 numerical simulation examples of Chua's circuit and 3‐cell cellular neural network with multiscroll chaotic attractors are presented to illustrate the effectiveness and advantages of the proposed theoretical results.

Event-triggered non-fragile state estimation for delayed neural networks with randomly occurring sensor nonlinearity

This paper is concerned with event-triggered non-fragile state estimator design for delayed neural networks subject to randomly occurring sensor nonlinearity. Different from the existing event-triggered scheme, a new event-triggered scheme is designed which is dependent on the incomplete measurement. The adopted event-triggered scheme is introduced between the neural networks and state estimator for the purpose of energy saving. Considering the sensor nonlinearity and using the event-triggered scheme, a new estimation error system is modeled. Based on this model, a sufficient condition is derived to guarantee the asymptotical stability of estimation error system. Furthermore, a desired event-triggered non-fragile estimator is designed by solving a set of linear matrix inequalities. Finally, a numerical example is provided to illustrate the usefulness of the proposed method.