Distributed Cooperative Fault Detection for Multiagent Systems: A Mixed $H_{\infty }/H_2$ Optimization Approach

Li, Yan; Fang, Hao; Chen, Jie; Yu, Chengpu

doi:10.1109/tie.2017.2786235

Cited by 58 publications

(17 citation statements)

References 32 publications

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“…For example, [5] and [6] proposed Unknown Input Observers (UIO) for detecting faults in a decentralized and distributed manner, respectively. In [7], a distributed fault estimation scheme was proposed by using H ∞ optimization, which involves sensitivity of faults and robustness of disturbances. In [13], a consensus-based decentralized observer is designed for fault detection.…”

Section: Introductionmentioning

confidence: 99%

Distributed $\ell _1$-State-and-Fault Estimation for Multiagent Systems

Hashimoto

Chong

Dimarogonas

2020

IEEE Trans. Control Netw. Syst.

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In this paper, we propose a distributed state-andfault estimation scheme for multi-agent systems. The estimator is based on an 1-norm optimization problem, which is inspired by sparse signal recovery in the field of compressive sampling. Two theoretical results are given to analyze the correctness of our approach. First, we provide a necessary and sufficient condition such that the state and fault signals are correctly estimated. The result presents a fundamental limitation of the algorithm, which shows how many faulty nodes are allowed to ensure a correct estimation. Second, we analyze how the estimation error grows over time by showing that the upper bound of the estimation error depends on the previous state estimate and the number of faulty nodes. An illustrative example is given to validate the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Distributed $\ell _1$-State-and-Fault Estimation for Multiagent Systems

Hashimoto

Chong

Dimarogonas

2020

IEEE Trans. Control Netw. Syst.

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“…For each k ∈ N, all followers obtain the measurement outputs according to (6) and transmit them to the leader over the communication network. Then, the leader estimates both the states of all nodes x(k) and the fault signals f (k), based on the knowledge of the dynamics and measurement outputs in (12) and (16).…”

Section: B Dynamics and Measurement Modelmentioning

confidence: 99%

Realtime l₁-fault-and-state estimation for multi-agent systems

Hashimoto

Chong

Dimarogonas

2019

2019 American Control Conference (ACC)

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In this paper, we propose a state and fault estimation scheme for multi-agent systems. The proposed estimator is based on an 1-norm optimization problem, which is inspired by sparse signal recovery in the field of compressive sampling. Moreover, we provide a necessary and sufficient condition such that state and fault signals are correctly estimated. The result presents a fundamental limitation of the algorithm, which shows how many faulty nodes are allowed to ensure a correct estimation. An illustrative example of a vehicle platoon is given to validate the effectiveness of the proposed approach.

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“…[19] dealt with the FD problem for the discrete-time Markovian jump linear system with a stochastic packet dropping effect. In [20] and [21], the H ∞ optimization was used for FD of linear timeinvariant systems where the generated residuals were sensitive to faults while robust against disturbances and noises. In [22] and [23], unknown input observers were used to solve the FD problems for second-order MASs.…”

Section: Introductionmentioning

confidence: 99%

Distributed Fault Detection for Linear Time-Varying Multi-Agent Systems With Relative Output Information

Zou

Wang

2021

IEEE Access

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This paper investigates the distributed fault detection problem for linear discrete time-varying heterogeneous multi-agent systems under relative output information. Due to the lack of absolute outputs, an augmented model is built by stacking all local relative output information. Then, the fault detection problem consisting of residual-generation and residual-evaluation is handled using the H ∞ filtering framework. The residual-generation problem is actually a minimization problem of an indefinite quadratic form, and the Krein space-Kalman filtering theory is applied, which results in a low computational burden despite the time-varying characteristic. Using the Krein space theory, a necessary and sufficient condition for the minimum is derived, and a residual-generation algorithm is developed. Further, a residual-evaluation mechanism is designed by constructing an evaluation function and detecting faults by comparing it with a threshold. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed fault detection approach.

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Distributed Cooperative Fault Detection for Multiagent Systems: A Mixed $H_{\infty }/H_2$ Optimization Approach

Cited by 58 publications

References 32 publications

Distributed $\ell _1$-State-and-Fault Estimation for Multiagent Systems

Distributed $\ell _1$-State-and-Fault Estimation for Multiagent Systems

Realtime l₁-fault-and-state estimation for multi-agent systems

Distributed Fault Detection for Linear Time-Varying Multi-Agent Systems With Relative Output Information

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Distributed Cooperative Fault Detection for Multiagent Systems: A Mixed $H_{\infty }/H_2$ Optimization Approach

Cited by 58 publications

References 32 publications

Distributed $\ell _1$-State-and-Fault Estimation for Multiagent Systems

Distributed $\ell _1$-State-and-Fault Estimation for Multiagent Systems

Realtime l1-fault-and-state estimation for multi-agent systems

Distributed Fault Detection for Linear Time-Varying Multi-Agent Systems With Relative Output Information

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Product

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Realtime l₁-fault-and-state estimation for multi-agent systems