Robust measurement fusion steady-state estimator design for multisensor networked systems with random two-step transmission delays and missing measurements

Effects of amino acid ionic liquids with different cations ([N2Py], [N2222], [P2222], and [C2mim]) on wheat seedlings

“…This may be due to the preferential root accumulation of ILs in plant. 9 Meanwhile, our results are consistent with Liu et al 21 and Xia et al, 14 who found that ILs caused damage to the ultrastructure of plants. Chen et al 16 also found that ILs with different anions mainly damage chloroplasts, mitochondria, cell walls, cell membranes, and nuclei but have minimal on the Golgi in root cells and wheat leaves, which is in good agreement with the results of this study.…”

State Estimation for a Class of Distributed Parameter Systems with Time-Varying Delay over Mobile Sensor–Actuator Networks with Missing Measurements

“…Assume there exist positive constants λ 1 , λ 2 , λ 3 , η, k i to make (12) and (13), then (25) can be gained by…”

“…The estimation with missing measurements is always an interesting and significant issue which has aroused many researchers' interests, and abundant achievements have been obtained. For instance, the main methods of Lyapunov function technique, stochastic analysis, or linear matrix inequality are used to solve the problem about missing measurements, such as in [10][11][12][13]. Some of the specific representative achievements for missing measurements are Kalman filter assimilation in [14], adaptive event-triggered H ∞ filter in [15], identification of jump Markov autoregressive exogenous systems in [16], and distributed recursive filtering for multi-sensor networked systems in [17].…”

“…Additionally, the distributed estimation with randomly occurring nonlinearities and missing measurements in [21], by making use of variance constraint states for stochastic coupling in [22], and recursive state estimator under the event-triggered mechanism in [23]. Moreover, robust fusion Kalman estimators for mixed uncertain systems [24] and a steady-state estimator with random two-step transmission delays and missing measurements in [13]. In addition, there are several methods that can be applied for parameter estimations in a distributed scenario, such as a novel resampling algorithms with architectures for efficient distributed implementation of particle filters [25], a distributed particle filter for nonlinear tracking in wireless sensor networks [26], the uniform convergence over time for a distributed particle filter [27], and the compressed Monte Carlo with application in particle filtering [28], which illustrated the specially particle filters are extremely flexible and powerful.…”

Robust Sequential Fusion Estimation Based on Adaptive Innovation Event-Triggered Mechanism for Uncertain Networked Systems

“…In [8][9][10], the local and fused robust time-varying Kalman predictors, flters, and smoothers were proposed for uncertain systems with uncertain noise variances. Te robust Kalman estimators were designed for sensor systems with mixed uncertainties in [11], which was achieved by converting the original system into a new system with uncertain noise variances and applying the augmented state, derandomization, and fctitious noise technologies.…”