Sequential Fusion Estimation for Networked Multisensor Nonlinear Systems

Zhang, Wen‐An; Zhou, Kang; Yang, Xusheng; Liu, Andong

doi:10.1109/tie.2019.2927191

Cited by 25 publications

(16 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Proof. If ( ) 1 ρ Φ  , similar to the proof of references [22,23], we easily prove that (34) hold by a direct application of the result of [24,25]. This completes the proof.…”

Section: Actual and Conservative State Second Non-center Momentssupporting

confidence: 64%

Centralized Fusion Robust Steady-state Estimator for Networked Uncertain System With Coloured Noises, One-step Random Delay and Packet Dropouts

Liu

Tao

2022

Preprint

View full text Add to dashboard Cite

This study is aimed to address the centralized fusion (CF) robust estimation problems for a class of multisensor networked systems with mixed uncertainties, which include coloured noises, same multiplicative noises in system parameter matrices, additive white noise, uncertain noise variances, as well as the one-step random delay and packet dropouts (PD). To deal with this kind of systems, one-step random delay and inconsecutive packet dropouts are described by two Bernoulli distributed random variables. By using the augmentation, de-randomization and fictitious noise techniques, the original system has transformed into an augment model with only uncertain noise variances. In the light of the minimax robust estimation principle, based on the worst-case CF system with conservative upper bounds of uncertain noise variances, the robust CF steady-state Kalman estimators (predictor, filter, and smoother) are presented. By providing a novel approach, consist of non-negative definite matrix method and Lyapunov equation approach, prove the robustness of the CF estimator. Finally, a UPS simulation example illustrates the effectiveness of the proposed CF estimation algorithms.

show abstract

“…Proof. If ( ) 1 ρ Φ  , similar to the proof of references [22,23], we easily prove that (34) hold by a direct application of the result of [24,25]. This completes the proof.…”

Section: Actual and Conservative State Second Non-center Momentssupporting

confidence: 64%

Centralized Fusion Robust Steady-state Estimator for Networked Uncertain System With Coloured Noises, One-step Random Delay and Packet Dropouts

Liu

Tao

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…One can conclude from ( 26), ( 28) and ( 30) that all the subsystems are asymptotically stable. Applying Dynkin's formula and Gronwall-Bellman inequality to (26), ( 28) and (30), the exponential convergence of the corresponding Lyapunov-Krasovskii functional can be obtained, i.e., there exist three scalars a j > 1, such that…”

Section: Resultsmentioning

confidence: 99%

“…Digital sampling devices, such as phasor measurement units, compute the active power of these tie lines and frequency of control areas by measuring voltage and current phasors of each generator. In practice, the data acquisition process needs to maintain a given sampling rate [28], and the sampling rate of these digital devices may be lower than the basic operating rate of the controlled plants [29], [30]. Thus only sampled-data will be obtained for control design.…”

Section: Adaptive Event Generator1mentioning

confidence: 99%

Event-Triggered Predictive Control for Networked Systems Based on a Dual-Rate Sampling Switched Observer

2021

View full text Add to dashboard Cite

This paper is concerned with predictive control of discrete-time networked control systems (NCSs) based on a dual-rate sampling switched observer, where the multiple output signals of the controlled plant are sampled using a dual-rate sampling mechanism. To reduce the effect of the bilateral networkinduced uncertainties on NCSs, a novel predictive control protocol is proposed to compensate for the bilateral network-induced delays and packet dropouts. First, a dual-rate sampled-data-based switched observer with a fixed switching law is designed to estimate the state of the controlled plant, where the switching law is determined by the dual-rate sampled instants. Second, an estimated-state-based predictive controller is designed by synchronizing the control inputs used by the state predictor and the controlled plant, and the networked predictive control system is formulated as a threshold-error-dependent delay system model, where two associated but different event-triggered transmission schemes are introduced to determine whether the estimated states or the control packet should be transmitted or not. Sufficient conditions for codesigning the event-triggered matrix and the predictive controller gain are derived by using the Lyapunov-Krasovskii functional method. Finally, it is shown through a numerical example that the proposed method in this paper is effective for ensuring better system performance.INDEX TERMS Networked control systems, Dual-rate sampling mechanism, Adaptive event-triggered transmission schemes, Predictive control

show abstract

“…A moving target tracking system in the WSNs is considered for simulation [24], as shown in Figure 1. The motion model of the target is given by…”

Section: A Simulation Examplementioning

confidence: 99%

“…A moving target tracking system in the WSNs is considered for simulation [24], as shown in Figure 1. The motion model of the target is given by

\begin{matrix} alignleft \\ align-1 [\begin{array}{c} x_{1} false(k + 1 false) \\ x_{2} false(k + 1 false) \\ x_{3} false(k + 1 false) \end{array}] = & align-2 [\begin{array}{c} x_{1} false(k false) \\ x_{2} false(k false) \\ x_{3} false(k false) \end{array}] + [\begin{array}{c} R ((), \sin ((), x_{3} false(k false)) + \frac{v}{R}) - \sin ((), x_{3} false(k false)) \\ R ((), \cos ((), x_{3} false(k false)) + \frac{v}{R}) - \cos ((), x_{3} false(k false)) \\ \frac{v}{R} \end{array}] \\ align-1 & align-2 + B (u_{k} + α_{a k}) + w_{k}, \\ align-1 y (k) = & align-2 [\begin{array}{c} x_{1} false(k false) \\ x_{2} false(k false) \\ x_{3} false(k false) \end{array}] + α_{s k} + v_{k}, \end{matrix}

where x 1 ( k ), x 2 ( k ), x 3 ( k ), y ( k ), and u k are the X‐coordinate, Y‐coordinate, angle, output, and input of the moving target at timestep k , respectively.…”

Section: A Simulation Examplementioning

confidence: 99%

An adaptive unscented Kalman filter approach to secure state estimation for wireless sensor networks

Miao

Zhang

Qiu

2022

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

Wireless sensor networks are vulnerable to false data injection attacks, which may mislead the state estimation. To solve this problem, this paper presents a chi‐square test‐based adaptive secure state estimation (CTASSE) algorithm for state estimation and attack detection. Taking advantage of Kalman filters, attack signal together with process noise or measurement noise are described as total white Gaussian noise with uncertain covariance matrix. The chi‐square test method is used in the adaptation of the total noise covariance and attack detection. Then, a standard adaptive unscented Kalman filter (UKF) is used for the state estimation. Finally, simulation results show that the proposed CTASSE algorithm performs better than other UKFs in state estimation and is also effective in real‐time attack detection.

show abstract

Sequential Fusion Estimation for Networked Multisensor Nonlinear Systems

Cited by 25 publications

References 33 publications

Centralized Fusion Robust Steady-state Estimator for Networked Uncertain System With Coloured Noises, One-step Random Delay and Packet Dropouts

Centralized Fusion Robust Steady-state Estimator for Networked Uncertain System With Coloured Noises, One-step Random Delay and Packet Dropouts

Event-Triggered Predictive Control for Networked Systems Based on a Dual-Rate Sampling Switched Observer

An adaptive unscented Kalman filter approach to secure state estimation for wireless sensor networks

Contact Info

Product

Resources

About