Asynchronous State Estimation Research Articles

Attack-resilient asynchronous state estimation of interval type-2 fuzzy systems under stochastic protocols

This article addresses the state estimator design issue of interval type-2 (IT2) fuzzy systems under deception attacks. The permission of sensors to access the network is determined by a stochastic protocol obeying Markov scheduling rules. An attack-resilient IT2 fuzzy state estimator is proposed to mitigate the adverse effect of deception attacks on estimation performance. By using the known bounds of system membership functions and introducing two new weighting functions, the MFs of fuzzy state estimator are reconstructed. A hidden Markov model is employed to describe the asynchronous behaviour of scheduling rules between the sensor and the estimator. Sufficient conditions are derived to ensure the ultimate boundedness of estimation error. Finally, a numerical example demonstrates the applicability of the proposed attack-resilient asynchronous state estimation method.

Event-based asynchronous state estimation for Markov jump memristive neural networks

This study endeavors to address the problem of asynchronous state estimation for generalized Markov jump memristive neural networks in the presence of a piecewise homogeneous Markov process and a memory-based dynamic event-triggered protocol. To avoid the commonly made assumption of time-invariant transition probabilities, a piecewise-homogeneous Markov chain is presented, wherein the variations of transition probabilities are regulated by a higher-level Markov process. Additionally, a memory-based dynamic event-triggered protocol is proposed to leverage the useful information contained in historically launched packets and achieve better control performance. Moreover, a hidden nonhomogeneous Markov model strategy is utilized to identify the modes mismatch between the estimator and the Markov jump memristive neural networks. This greatly reduces the complexity of processing the mode information of the system. Eventually, to verify the effectiveness of the theoretical results, a simulation example is carried out.

Finite-time [formula omitted] asynchronous state estimation for stochastically switched delayed genetic regulatory networks with sojourn probabilities

The issue of finite-time H∞ asynchronous state estimation is studied for stochastically switched delayed genetic regulatory networks (GRNs) with sojourn probabilities. A new asynchronous state estimator is proposed, where the mode’s sojourn probabilities are stochastic and dependent on the mode of the GRN. By constructing a Lyapunov–Krasovskii functional that contains the mode of GRN and using an auxiliary functions-based summation inequality, a sufficient condition described by certain matrix inequalities is given to guarantee the error system to be stochastically finite-time bounded with H∞ performance. The gain matrices of state estimator are designed via the feasible solutions of these matrix inequalities. The merit of the state estimator design is demonstrated by an example.

Asynchronous and Adaptive State Estimation of Integrated Electricity–Gas Energy Systems

The combination of Internet of Things technology and power grid technology greatly strengthens the situational awareness in integrated energy systems. However, present development fails to consider the internal dynamic characteristics of the natural gas system. This is addressed in the present study by proposing an asynchronous distributed dynamic state estimation method for integrated electricity-gas energy systems. First, static state estimation based on a weighted least square method is applied to the electric power network, and an improved extended Kalman filter state estimation method based on a transient model is applied to the natural gas network. The asynchronous exchange of information between the two subsystems is addressed by applying an iterative update step correction to the dynamic state estimation of the natural gas network. Second, we propose an adaptive state estimation execution cycle adjustment method to track the state of the natural gas subsystem in real time under conditions of large disturbances in that subsystem. Application of the proposed algorithm to simulation case studies demonstrates that it can greatly improve the estimation accuracy and trajectory tracking performance of the state estimation process relative to conventional methods.

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.

A parallel and asynchronous state estimation for coupled transmission-distribution networks

Considering the increasing scale of power systems and gradually coupling between transmission networks (TNs) and distribution networks (DNs), a parallel and asynchronous state estimation (par&async-SE) algorithm is proposed to deal with communication delays. First, the coupled transmission-distribution network state estimation problem is reformulated in line with engineering practice. Then, a TN broadcast transmitting strategy, a synchronous preprocessing technique, and a receive-compute-transmit parallel mechanism are applied to improve conventional asynchronous algorithm. Moreover, a ‘key DN’ concept is introduced and a sensitivity analysis method is proposed to realize key DNs selection. These improvements make the par&async-SE algorithm proposed not only have a higher convergence rate and efficiency but also be less sensitive to parameter settings. Numerical experiments demonstrate that the par&async-SE algorithm has the same accuracy as conventional algorithms. It can also achieve better convergence, reduce computation time, and be less sensitive to parameter settings than the synchronous state estimation algorithm and conventional asynchronous state estimation algorithm when communication delays exist, so that parameter tuning for this self-adaptive algorithm is not necessary in engineering practice. Besides, the par&async-SE algorithm also has bad data robustness when choosing appropriate methods to solve extended-TNSE and DNSE subproblems.

Finite‐time adaptive event‐triggered asynchronous state estimation for Markov jump systems with cyber‐attacks

AbstractThis article considers the issue of finite‐time asynchronous state estimation for event‐triggered nonlinear Markovian jump systems subject to cyber‐attacks. An adaptive event‐triggered scheme is introduced to cope with the capacity constraint of the networked resources. It is assumed that the transmitted sensor measurements may experience randomly malicious cyber‐attacks. Considering the effect of the adaptive event‐triggered scheme and the occurrence of cyber‐attacks, we establish a new state estimation error system model. Sufficient conditions of the finite‐time boundedness and the finite‐time boundedness are developed for the augmented system. Then, the design methods of the asynchronous estimator gains are derived, which can ensure the finite‐time boundedness of the estimation error system. Finally, a numerical example is given to show the effectiveness of the theoretical results.

Finite-time asynchronous state estimation for jump systems with partial transition probabilities via redundant channels: A co-design method

This paper addresses the H∞ finite-time asynchronous state estimation issue for Markov jump systems with partial transition probabilities. A hidden-Markov-chain-based redundant channel model (HMCb-RCM) is established to reflect a more practical situation. Based on the output of the HMCb-RCM, firstly an asynchronous full-order state estimator is devised for the jump system with partial transition probabilities. Then, new sufficient criteria are derived such that the state estimation error is H∞ stochastically finite-time bounded. The relationship between the partial transition probabilities and asynchronous modes is revealed as few attempts. The conditional transition probability matrix (CTPM) for the HMCb-RCM is not fixed but designable accordingly; a co-design strategy is newly developed to synthesize the CTPM and the state estimator simultaneously, which produces less conservatism than that with fixed CTPM. Finally, the theoretical results are applied to a one-link robotic manipulator to validate the proposed results.

Non-fragile asynchronous state estimation for Markovian switching CVNs with partly accessible mode detection: The discrete-time case

This article is devoted to the non-fragile asynchronous state estimation problem for Markovian switching complex-valued networks subject to randomly occurring nonlinearities (RONs) and external stochastic disturbances. Two mutually uncorrelated random variables with known statistical properties are introduced to depict the existing RONs. Considering the case that the information concerning to the system modes cannot be completely acquired by the state estimator to be designed, the asynchronous phenomenon is considered via a hidden Markov model with only partial mode detection probabilities being accessible. By resorting to the intensive stochastic analysis as well as an improved complex-valued reciprocal convex inequality, some mode-dependent criteria are provided which ascertain that the estimation error system is asymptotically mean square stable. In addition, the estimator gains desired can be appropriately designed by resorting to feasible solutions of a set of complex matrix inequalities. One numerical example is also provided to demonstrate effectiveness of the estimation scheme proposed.

Diffusion event-triggered sequential asynchronous state estimation algorithm for stochastic multiplicative noise systems

This article focuses on the problem of the diffusion event-triggered sequential asynchronous state estimation for the stochastic multiplicative noise systems. In this paper, we propose a diffusion sequential asynchronous state estimation algorithm for each node to obtain the final estimate. In order to ease the communication burden of the system, we propose a two-stage event-triggered mechanism to avoid the unnecessary information transmissions, and the corresponding state estimation algorithm is derived. The sufficient conditions to guarantee the boundedness of the estimation error of the proposed algorithm are established. The effectiveness of the proposed algorithm is verified through its application to a target tracking system.

Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities

This paper addresses the finite-time state estimation problem for semi-Markovian jump neural networks with sensor nonlinearities under the consideration of leakage delay and time-varying delay. The modes of original system and desired estimator are supposed to be asynchronous. Some sufficient conditions are proposed to guarantee the finite-time boundedness as well as mixed H∞ and passive performance of the error system in terms of constructing Lyapunov–Krasovskii functionals. By utilizing affine Bessel-Legendre inequalities, a less conservative result can be achieved. By virtue of linear matrices inequalities approach, the asynchronous state estimator gains are obtained. Two numerical examples are provided to demonstrate the less conservativeness and effectiveness of our method.

Resilient Asynchronous State Estimation for Markovian Jump Neural Networks Subject to Stochastic Nonlinearities and Sensor Saturations.

This article studies the problem of dissipativity-based asynchronous state estimation for a class of discrete-time Markov jump neural networks subject to randomly occurring nonlinearities, sensor saturations, and stochastic parameter uncertainties. First, two stochastic nonlinearities occurring in the system are described by statistical means and obey two Bernoulli processes independently. Then, the hidden Markov model is used to characterize the real communication environment closely between the designed estimator and the system model due to the networked-induced phenomenons that also lead to randomly occurring parametric uncertainties of the estimator considered modeled by two Bernoulli processes. A new criterion is established to guarantee that the resulting error system is stochastically stable with predefined dissipativity performance. Finally, we provide a simulation example to validate the theoretical analysis.

Asynchronous Multi-Sensor State Estimation for Systems Subject to Multiplicative and Cross-Correlated Noises With Measurement Packet Dropping

This paper is concerned with the optimal state estimation problem for linear discrete-time systems with both multiplicative and cross-correlated noises. The measurement outputs for state estimation are collected from multiple sensors whose sampling rates are different that provide asynchronous data. In addition, the noises that affect the measurement information are correlated among different sensors and also coupled with the process noises as well. The aim of the addressed problem is to propose an optimal state estimation algorithm such that the estimation error is minimized in the mean-square sense with the existence of asynchronous data, possible sensor faults and correlated noises. In order to mitigate the impact of measurement missing, this paper utilizes neural networks to compensate the state estimation when measurement packets are dropping. Then, a fault detection mechanism that utilizes normalized innovation test is adopted to ensure that the abnormal data would be detected and removed. By resorting to the projection theorem and mathematical induction approach, sufficient conditions are derived for the existence of the desired optimal state estimator where the optimized estimation gains are formulated and can be computed iteratively at each time step. The proposed theoretical results are demonstrated via an illustrative numerical example.

Resilient asynchronous state estimation of Markov switching neural networks: A hierarchical structure approach

This paper deals with the issue of resilient asynchronous state estimation of discrete-time Markov switching neural networks. Randomly occurring signal quantization and packet dropout are involved in the imperfect measured output. The asynchronous switching phenomena appear among Markov switching neural networks, quantizer modes and filter modes, which are modeled by a hierarchical structure approach. By resorting to the hierarchical structure approach and Lyapunov functional technique, sufficient conditions are achieved, and asynchronous resilient filters are derived such that filtering error dynamic is stochastically stable. Finally, two examples are included to verify the validity of the proposed method.

H∞ state estimation via asynchronous filtering for descriptor Markov jump systems with packet losses

Abstract This paper is concerned with the problem of asynchronous state estimation for a class of discrete-time descriptor Markov jump systems with packet losses. Packet losses and asynchronous modes are simultaneously considered in the communication link between the plant and the estimator, where the missing probability of packet losses is governed by a binary distribution. Firstly, by constructing a stochastic Lyapunov functional, a sufficient condition is given such that the filtering error system is stochastically admissible and has a prescribed H∞ noise attenuation performance index. Then, based on the matrix inequality decoupling technique, a linear matrix inequality (LMI)-based condition on the existence of the H∞ asynchronous filter is presented, where the asynchronous filter can be of full-order or reduced-order. Compared with the previous methods, the proposed design method does not impose constraints on slack variables and dimensions of the designed filter, which shows less conservative results. Finally, numerical examples are given to illustrate the benefit and applicability of the new method.

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.

Finite-time [formula omitted] asynchronous state estimation for discrete-time fuzzy Markov jump neural networks with uncertain measurements

This paper is concerned with the problem of the H∞ asynchronous state estimation for fuzzy Markov jump neural networks (FMJNNs) with uncertain measurements over a finite-time interval. In terms of a Bernoulli distributed white sequence, the phenomenon of the randomly occurring uncertainties in the output equation is represented by exploiting a random variable with known occurrence probabilities. The main focus of this paper is to present a state estimator such that the resulting error system is finite-time bounded and satisfies an H∞ performance requirement. Then, by employing the stochastic analysis technique, sufficient conditions are provided to ensure that the state estimator is designed by means of solving a convex optimization problem. An example is finally given to explain the effectiveness and potentiality of the proposed design method.

State estimation for asynchronous sensor systems with Markov jumps and multiplicative noises

This paper is concerned with the asynchronous state estimation problem for sensor systems subject to Markov jump parameters and multiplicative noises. The asynchronous sensors considered can have arbitrary sampling rates and arbitrary initial sampling instants. By transforming the asynchronous measurements within each fusion interval into an augmented measurement, the equivalent measurement equation and state equation at the fusion time instant are constructed. Based on the two equations, a linear minimum mean-squared error (LMMSE) estimator is developed using the orthogonality projection principle. Due to the existence of the common process noise in the equivalent process and measurement noises, the equivalent process and measurement noises are cross-correlated and the equivalent measurement noises are autocorrelated. These correlations are taken into account in the estimator design. The stationary case is also studied and the sufficient condition is established for the stability of the proposed estimator. A target tracking example is provided to illustrate the effectiveness of the proposed estimator.

Asynchronous State Estimation for Discrete-Time Switched Complex Networks With Communication Constraints.

This paper is concerned with the asynchronous state estimation for a class of discrete-time switched complex networks with communication constraints. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the topology/coupling information. Also, the event-based communication, signal quantization, and the random packet dropout problems are studied due to the limited communication resource. With the help of switched system theory and by resorting to some stochastic system analysis method, a sufficient condition is proposed to guarantee the exponential stability of estimation error system in the mean-square sense and a prescribed performance level is also ensured. The characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem. Finally, the effectiveness of the proposed design approach is demonstrated by a simulation example.

Dissipativity-based asynchronous state estimation for Markov jump neural networks with jumping fading channels

The problem of asynchronous state estimation for Markov jump neural networks taking into account jumping fading channels is investigated in this article. The phenomenon of channel fadings which occurs between the system and the state estimator is considered and a modified discrete-time Rice fading model with the mode-dependent channel coefficients is adopted. Due to the fact that the modes of system can not be completely accessible to the state estimator at any time, the asynchronous state estimator which can make full use of the partial information available to the state estimator is introduced. By using the mode-dependent Lyapunov functional approach, some sufficient conditions for the existence of asynchronous state estimator of the Markov jump neural networks are given to guarantee the stability and dissipativity of the augmented system. The gains of asynchronous state estimator are given via solving a set of linear matrix inequalities. The merits and effectiveness of the developed design scheme are verified by a simulation example.