A Distributed Fault Estimation Approach for a Class of Continuous-Time Nonlinear Networked Systems Subject to Communication Delays

Rahimi, Farshad; Rezaei, Hossein

doi:10.1109/lcsys.2021.3071478

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…Comparative simulation results demonstrate that the RMSEs are significantly decreased in our method in comparison to the existing methods. Extending the developed filtering strategy for dynamical systems with the fault estimation 37 and considering more imperfections in the communication medium, such as transmission delay [38][39][40] define future research line.…”

Section: Discussionmentioning

confidence: 99%

Event‐triggered resilient distributed extended Kalman filter with consensus on estimation

Rezaei

Ghorbani

2021

Intl J Robust & Nonlinear

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This study investigates the event-triggered resilient recursive distributed state estimation problem for discrete-time nonlinear systems over sensor networks.An event-triggered mechanism is employed to save the limited computation resource and network bandwidth while maintaining the desired performance. A resilient Extended Kalman Filter (EKF) with consensus on estimations is developed, and consensus is first achieved with respect to the prediction estimation. The accuracy of the computed estimation is then improved via two recursive equations. By adopting the structure of the EKF, the filter gains are determined in each sensor node via utilization of an upper bound for the cross-covariance, thereby resulting in a lower computational burden. The boundedness of the estimation errors is analyzed. Simulation results are reported to illustrate the effectiveness of the proposed distributed filter.

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Section: Discussionmentioning

confidence: 99%

Event‐triggered resilient distributed extended Kalman filter with consensus on estimation

Rezaei

Ghorbani

2021

Intl J Robust & Nonlinear

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“…Comparative simulation results have been reported to show that the averaged root mean square errors (RMSE) of estimation gets decreased by our method compared to a rival one in literature. The main results of this paper can be developed to the following issues in order to improve the proposed method in the future works: (1) consideration of the roundrobin protocol for reducing the data exchange between nodes [19], [33], (2) attachment of the gain variations that usually occur in the practical system because of computational or implementation uncertainties in the hardware [34], [35], (3) consideration of the issue that when data is exchanged among the sensors, due to the vulnerability of communication networks, the information can be overheard and modified by the adversary [36], [37], (4) consideration of the Fault Estimation [38] . Thus, based on the view of the author, considering the above topics can improve the performance of the proposed method in practical applications.…”

Section: Discussionmentioning

confidence: 99%

Scalable Distributed State Estimation for a Class of State-Saturated Systems Subject to Quantization Effects

Rezaei

Farnam²,

Rahimi

et al. 2021

IEEE Access

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This paper investigates the problem of a scalable distributed state estimation for a class of discrete time-variant systems with state-saturation, quantization effects, and two redundant channels over a sensor network. In transmission data from a sensor to its estimator, two phenomena are considered together. First, the data of each sensor is transmitted to its estimator through two redundant communication channels. Second, innovation data is quantized before being used by the estimator. These phenomena are beneficial in alleviating the negative effects on measurements and reducing the energy consumption and bandwidth. In the structure of proposed filter consensus is used on estimations in which consensus is first achieved on the prediction estimation, then the accuracy of computed estimation is improved by two recursive equations. The parameters of the proposed filter are obtained for each sensor node by employing an upper bound for common error covariance, therefore less computational burden is required. Eventually, the comparative simulation results are presented to show that our method has better performance compared with a rival one recently published.

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“…The majority of works that have been published in regard to the problem of the fault occurring concentrate on the centralized structure which are not suitable for implementing in multiagent systems. Recently, many researchers have achieved fruitful outcomes in deriving and analysis of the fault diagnosis schemes in multi-agent systems (see, e.g., references [9][10][11][12][13][14][15] and the references therein). The fault diagnosis approaches can be classified into three main topics: the fault detection, the fault isolation, and the fault estimation [16,17].…”

Section: Introductionmentioning

confidence: 99%

Neighborhood‐based distributed robust unknown input observer for fault estimation in nonlinear networked systems

Rahimi

Ahmadpour

2022

IET Control Theory & Appl

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In this paper, a robust distributed fault estimation is addressed for a class of nonlinear networked systems with actuator faults where influences from process disturbances are minimized. In real‐time monitoring systems, a substantial challenge is to find out the size and the shape of the occurred faults, and consequently, it is significantly more difficult in multi‐agent systems. To consider this concern, in this paper, a distributed fault estimation approach has been proposed for multi‐agent systems such that each agent employs an augmented system owing to a designated communication graph for estimating the fault and states both in this agent and in its neighbors. Meanwhile, each agent is equipped with an unknown input observer (UIO) to decouple the partial disturbances of the process as much as possible and to reduce the disturbances that cannot be decoupled. In the form of linear matrix inequalities (LMIs), sufficient conditions are provided to guarantee stability and to obtain the parameter matrices of the introduced observer. Finally, two numerical examples are provided to verify the performance of the introduced fault estimation scheme.

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A Distributed Fault Estimation Approach for a Class of Continuous-Time Nonlinear Networked Systems Subject to Communication Delays

Cited by 16 publications

References 20 publications

Event‐triggered resilient distributed extended Kalman filter with consensus on estimation

Event‐triggered resilient distributed extended Kalman filter with consensus on estimation

Scalable Distributed State Estimation for a Class of State-Saturated Systems Subject to Quantization Effects

Neighborhood‐based distributed robust unknown input observer for fault estimation in nonlinear networked systems

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