Dissipative State Estimation Research Articles

Event-triggered dissipative state estimation for Markov jump neural networks with random uncertainties

This paper is concerned with the problem of event-triggered dissipative state estimation for Markov jump neural networks with random uncertainties. The event-triggered mechanism is introduced to save the limited communication bandwidth resource and preserve the desired system performance. The phenomenon of randomly occurring parameter uncertainties is considered to increase utilizability of the proposed method. To describe such a randomly occurring phenomenon, some mutually independent Bernoulli distributed white sequences are adopted. A mode-dependent state estimator is designed in this paper, which ensures that the estimation error system is extended stochastically dissipative. By using the Lyapunov–Krasovskii functional approach and an optimized decoupling approach, an expected state estimator can be built by solving some sufficient conditions. Two numerical examples are presented to demonstrate the correctness and effectiveness of the proposed method.

Dissipative non-fragile state estimation for Markovian complex networks with coupling transmission delays

Abstract This paper studies the problem of non-fragile state estimator design for Markovian complex networks with coupling transmission delays, where the uncertainties exist in both the transition probability matrix and the estimator parameter. Furthermore, the estimator parameter uncertainty is supposed to be node- and mode-dependent. By constructing a parameter-dependent Lyapunov function, two sufficient conditions with less conservativeness are developed, which ensure the stochastic stability and the strictly ( Q - S - R )-γ-dissipative performance of the estimation error system. Then, the dissipative non-fragile state estimator is designed successfully via linear matrix inequality method. Finally, a simulation example is given to illustrate the effectiveness of the theoretical results.

Design of extended dissipativity state estimation for generalized neural networks with mixed time-varying delay signals

This paper investigates the issue of extended dissipativity state estimation of generalized neural networks (GNNs) with mixed time-varying delay signals. The integral terms in the time derivative of the Lyapunov–Krasovskii functionals (LKFs) are estimated by the famous Jensen’s inequality, reciprocally convex combination (RCC) approach together with the Wirtinger double integral inequality (WDII) technique. In addition, in order to estimate the double integral terms in the derivative of the LKF, a new integral inequality is proposed. As a result, a new delay-dependent criterion is derived under which the estimated error system is extended dissipative. The concept of extended dissipativity state estimation can be applied to deal with the L2−L∞ state estimation, H∞ state estimation, passivity state estimation, mixed H∞ and passivity state estimation, (Q,S,R)−γ-dissipativity state estimation of GNNs by choosing the weighting matrices. The advantage of the proposed method is demonstrated by five numerical examples, among them one example was supported by real-life application of the benchmark problem that is associated with reasonable issues in the sense of an extended dissipativity performance.

Extended dissipativity state estimation for switched discrete-time complex dynamical networks with multiple communication channels: A sojourn probability dependent approach

In this paper the problem of extended dissipative state estimation for discrete-time switched complex dynamical networks (CDNs) with mixed time delays is investigated. The switching approach is based on sojourn probabilities and it is assumed that these probabilities are known aprior. One primary channel and multiredundant channels which constitute multiple communication channels are considered to coexist for the state estimation of underlying switched CDNs. To solve for the H∞, l2−l∞, passive and dissipative state estimation, the concept of the extended dissipativity is used by adjusting the weighting matrices in a new performance index. Suitable Lyapunov–Krasovskii functional is constructed in terms of Kronecker product and based on the Lyapunov stability theory, new delay-dependent sufficient stability conditions are derived in terms of linear matrix inequalities (LMIs). The effectiveness of the developed theoretical results is demonstrated via a numerical example.

Extended dissipative state estimation for uncertain discrete-time Markov jump neural networks with mixed time delays

This paper is concerned with the problem of extended dissipativity-based state estimation for uncertain discrete-time Markov jump neural networks with finite piecewise homogeneous Markov chain and mixed time delays. The aim of this paper is to present a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative. A triple-summable term is introduced in the constructed Lyapunov function and the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term. The extended dissipativity criterion is derived in form of linear matrix inequalities. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.

Extended dissipative state estimation for memristive neural networks with time-varying delay

This paper investigates the problem of extended dissipative state estimation for memristor-based neural networks (MNNs) with time-varying delay. Based on both nonsmooth analysis and the construction of a new Lyapunov–Krasovskii functional, the extended dissipative state estimation criteria are obtained by mainly applying differential inclusions, set-valued maps and many new integral inequalities. The extended dissipative state estimation can be adopted to deal with l2−l∞ state estimation, H∞ state estimation, passive state estimation and dissipative state estimation by valuing the corresponding weighting matrices. Finally, two numerical examples are given to show the effectiveness and less conservatism of the proposed criteria.

Dissipativity Analysis for Discrete Time-Delay Fuzzy Neural Networks With Markovian Jumps

This paper is concerned with the dissipativity analysis and design of discrete Markovian jumping neural networks with sector-bounded nonlinear activation functions and time-varying delays represented by Takagi–Sugeno fuzzy model. The augmented fuzzy neural networks with Markovian jumps are first constructed based on estimator of Luenberger observer type. Then, applying piecewise Lyapunov–Krasovskii functional approach and stochastic analysis technique, a sufficient condition is provided to guarantee that the augmented fuzzy jump neural networks are stochastically dissipative. Moreover, a less conservative criterion is established to solve the dissipative state estimation problem by using matrix decomposition approach. Furthermore, to reduce the computational complexity of the algorithm, a dissipative estimator is designed to ensure stochastic dissipativity of the error fuzzy jump neural networks. As a special case, we have also considered the mixed $H_{\infty }$ and passive analysis of fuzzy jump neural networks. All criteria can be formulated in terms of linear matrix inequalities. Finally, two examples are given to show the effectiveness and potential of the new design techniques.

Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and Uncertain Measurements.

This paper addresses the problem of state estimation for a class of discrete-time stochastic complex networks with a constrained and randomly varying coupling and uncertain measurements. The randomly varying coupling is governed by a Markov chain, and the capacity constraint is handled by introducing a logarithmic quantizer. The uncertainty of measurements is modeled by a multiplicative noise. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the coupling information, and an augmented estimation error system is obtained using the Kronecker product. Sufficient conditions are established, which guarantee that the estimation error system is stochastically stable and achieves the strict (Q, S, R)-γ-dissipativity. Then, the estimator gains are derived using the linear matrix inequality method. Finally, a numerical example is provided to illustrate the effectiveness of the proposed new design techniques.

Robust dissipativity and passivity based state estimation for discrete-time stochastic Markov jump neural networks with discrete and distributed time-varying delays

In this paper, the problem of robust state estimation for discrete-time stochastic Markov jump neural networks with discrete and distributed time-varying delays is investigated based on dissipativity and passivity theory. The parameters of the neural networks are subject to the switching from one mode to another according to a Markov chain. By using the Lyapunov---Krasovskii functional together with linear matrix inequality approach, a new set of sufficient conditions are derived for the existence of state estimator such that the error state system is strictly $$(\mathcal {Q}, \mathcal {S}, \mathcal {R})-\gamma $$(Q,S,R)-?-dissipative. Finally, numerical examples are addressed to show the effectiveness of the proposed design method .