Event-Triggered Recursive State Estimation for Stochastic Complex Dynamical Networks Under Hybrid Attacks.

This paper deals with the recursive state estimation issue for mobile robot localization under a dynamic event-based mechanism. To enhance the utilization of communication resources, a dynamic event-based transmission protocol is utilized to reduce unnecessary measurement transmissions by introducing an auxiliary dynamical variable to adjust threshold parameters. The primary objective of this paper is to develop a dynamic event-based recursive state estimation scheme for the mobile robot localization problem in the presence of the impact of the dynamic event-based mechanism such that an upper bound on the estimation error covariance is firstly guaranteed by using mathematical induction and then is locally minimized by virtue of appropriately choosing the gain parameters. Furthermore, the boundedness analysis of the estimation error is conducted by establishing an evaluation criteria in the mean-squared sense. Finally, an experimental example is conducted to verify the feasibility of the proposed mobile robot localization strategy.

This paper investigates the recursive state estimation for time-varying systems under time-correlated fading channels and multiple description coding scheme in the presence of observation delays. The original system with observation delay is firstly transformed into a delay free system by using the reorganized observation method. The measurements are transmitted through a wireless channel, where the channel coefficient exhibits time-dependent fading. For this reason, auxiliary variables are introduced, and an augmented system is constructed to represent both the system state and the channel fading coefficients. Furthermore, a dual-description coding scheme is utilized to encode the time-correlated fading measurements into two equally significant descriptions. A recursive state estimator is first developed to ensure an upper bound on the estimation error covariance. This estimator innovatively integrates both time-correlated channel fading and multiple description coding methods, making it suitable for online computation. Then, the upper bound is minimized by selecting appropriate estimator gain parameters.

This paper studies the state estimation problem for nonlinear systems with missing measurements and filter‐and‐forward relays. By introducing stochastic variables obeying the Bernoulli distribution, the phenomenon of randomly occurring missing measurements is taken into consideration. The filter‐and‐forward relay is implemented in the channel from the sensor to the remote estimator, enhancing the quality of signal transmission. A matrix‐inequality‐based approach is proposed to design the filter in the filter‐and‐forward relay under which the filtering error in the relay is ensured to be ultimately bounded. Subsequently, a recursive estimator is built by employing the extended Kalman filter method. An upper bound on the estimation error covariance is derived by solving Riccati‐like difference equations and is then minimized by choosing an appropriate estimator gain. Finally, the validity of the estimation method is verified through two examples.

In this paper, the distributed state estimation problem is investigated for a class of discrete time-varying nonlinear systems over sensor networks with sensor resolution, where a binary encoding scheme is employed to address bandwidth limitations. The measurement output is converted into a binary bit string, conveyed through a memoryless binary symmetric channel subject to a certain probability of bit flips, and then reconstructed by a decoder. The influence of both sensor resolution and binary encoding scheme on estimation accuracy is considered simultaneously, where a distributed recursive state estimation strategy over sensor networks is constructed. Subsequently, the estimator gain that minimizes the upper bound on the estimation error covariance is obtained by solving Riccati-like difference equations. Finally, the validity of the developed algorithm is displayed through a simulation example.

Joint state and actuator fault estimation for networked systems under improved accumulation-based event-triggered mechanism

This paper discusses the encryption-decryption-based state estimation (EDBSE) issue for coupled perturbation complex networks (CPCNs) in the framework of the Kalman-type filtering scheme. A uniform distributed random variable is employed to characterize the coupled perturbation among different network units. A uniform-quantization-dependent encryption-decryption (UQDED) scheme is considered here to orchestrate the transmitted data. A novel EDBSE approach is developed such that the upper bounds of prediction error (PE) covariance (PEC) and estimation error (EE) covariance (EEC) can be derived by resolving Riccati-like difference equations and the estimation parameter (EP) is determined by minimizing the trace of the upper bound of EEC. Furthermore, a uniformly bounded condition is elaborated to evaluate the algorithm performance of EDBSE. Finally, an illustrative example is conducted to verify the validity of the introduced EDBSE method.

SummaryThe event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments.

This paper deals with recursive state estimation for nonlinear systems. A new set of σ-points for the Unscented Kalman Filter is proposed as well as a way to substitute a nonlinear output with a linear one. The importance of the function of the state which must be estimated is also illustrated and the need for taking it into account when designing the state estimator. All the proposed methods are compared with standard Extended Kalman Filter, Unscented Kalman Filter and Particle Filter with Sampling Importance Resampling using simulations. The results show that the modifications proposed in some cases lead to considerable reduction in estimation error.

This article examines the recursive estimation issue for a class of complex networks that incorporates decode-and-forward (DaF) relays and energy harvesting (EH) techniques. The random intercoupling topologies are captured by Gaussian noise. Owing to the insufficient transmission capacity of sensors, DaF relays are implemented to connect sensors with remote estimators, augmenting the transmission range and improving communication quality. The energy required for signal transmission can be supplied through EH techniques deployed at sensors and relays. This study focuses on designing a recursive state estimator aimed at guaranteeing accurate estimation performance. An upper bound for the estimation error covariance matrix is formulated via two recursive equations, and subsequently minimized by properly designing the estimation gain. Moreover, the developed estimator is evaluated through detailed theoretical analysis, with emphasis on its uniform boundedness and monotonic behavior. Simulated examples confirm the efficacy of the underlying distributed estimator.

DECENTRALIZED ROBUST BAD-DATA PRE-CLEANING WITH SUBSEQUENT HIERARCHICAL FAST RECURSIVE ESTIMATION

We consider the problem of distributed estimation for stochastic linear systems with intermittent observations. An optimal diffusion Kalman filter has been derived by minimizing the mean-squared estimation error for each node. Convergence of the estimation error covariance is proved under some mild assumptions and an upper bound is obtained for the estimation error covariance. A critical value for the arrival rate of observations is provided such that the estimation error covariance is bounded. The effectiveness of the proposed filter is validated via a numerical example involving tracking a moving target in a sensor network.

This brief is concerned with the joint state and fault estimation problem for a class of multi-rate systems with dynamical bias. To reflect real practice, the multi-rate sampling is considered which allows the sensor sampling rate and the state update rate to be different. The sensor is subject to the sensor fault that changes according to a dynamic equation. Instead of applying the traditional lifting technique, we introduce a time-varying delay into the measurement equation so as to transform the multi-rate systems into single-rate ones. The aim of this brief is to develop a joint state and fault estimation algorithm with minimized estimation error covariance. The recursion of the estimation error covariance is first derived, and appropriate estimator gains are then characterized that minimizes the estimation error covariance. A simulation example on the DC servo system is given to confirm the usefulness of the developed recursive state and fault estimation algorithm.

Hierarchical and Fast Recursive State Estimation with Robust Bad Data Pre-Cleaning for Large-Scale Power Systems

Event-based distributed resilient control strategy for microgrids subject to disturbances and hybrid attacks

Information disclosure and cyber attacks pose significant challenges to practical implementation of distributed control in microgrids (MGs). This paper investigates the distributed privacy-preserving resilient secondary control problem for an isolated AC MG under hybrid attacks including false data injection (FDI) and denial-of-service (DoS) attacks at low communication costs. Specifically, using state decomposition strategy and event-triggered mechanism, a distributed privacy-preserving event-triggered resilient secondary controller is designed, which reduces communication while safeguarding system privacy. Additionally, a long short-term memory (LSTM)-based estimator is designed to estimate the aggregated communication signals from neighboring DGs. The proposed resilient control method can further mitigate the impact of cyber attacks by using the estimated aggregated communication signals as the reference value, and the stability of the MG system is proven by Lyapunov theory. Compared to existing data-driven methods for attack detection, the proposed LSTM-based estimator uses communication signals as the database, thus avoiding the leakage of complete system data during offline training. To validate the effectiveness of the proposed method, a hardware-in-the-loop experiment is conducted under various hybrid attacks in OPAL-RT.