Distributed Robust Fault Estimation Using Relative Measurements for Leader–Follower Multiagent Systems

Luo, Ming; Fang, Hao; Li, Yan; Bai, Ye; Chen, Jie; Wei, Yue

doi:10.1109/tcyb.2019.2943522

Cited by 18 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For model training, the data within [10][11][12] s are selected for training and testing; 200 groups of data are used for training, 100 from the normal state, and100 from agent i; i ∈ [1, 2, … , 8] is faulty; the other 200 same groups of data are used for testing. The generation number is set to 100 for the optimization model.…”

Section: Design Of Fault Diagnosis Methods For Masmentioning

confidence: 99%

“…The authors in [10] designed a fixed‐time observer to obtain the auxiliary variable from neighbors to achieve fault diagnosis for nonlinear MASs with actuator faults. The authors in [11] considered that the measurements from agents may be not available and thus used the local relative information and the state estimation from neighbors to construct a distributed fault estimator. Similarly, the authors in [12] used relative information between the agent and its neighbors to construct an observer and then successfully implemented fault detection and isolation of MAS.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new belief rule base based distributed online fault diagnosis method for multi‐agent systems

Wang

Yang

et al. 2023

Asian Journal of Control

View full text Add to dashboard Cite

In this paper, based on belief rule base (BRB), we studied the distributed online fault diagnosis problem of multi‐agent systems (MASs). To begin with, considering the distributed nature of MASs, by using the relative information between an agent and its neighbors, a distributed online fault diagnosis model is constructed. Next, for the results of fault diagnosis, when an agent doesn't get enough relative information to diagnose the fault, the fault diagnosis result may be inaccurate, and if there exist too many neighbors of an agent, the number of attributes of the proposed BRB fault diagnosis model will be too large, which will lead to large computation. To improve the performance of the proposed distributed online fault diagnosis model, a real‐time communication topology switching strategy was proposed. Under the constructed distributed fault diagnosis model, the real‐time performance and the accuracy of fault diagnosis for agents can be guaranteed simultaneously. Finally, a simulation is given to validate the effectiveness and advancement of the distributed online fault diagnosis method proposed in this paper.

show abstract

Section: Design Of Fault Diagnosis Methods For Masmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new belief rule base based distributed online fault diagnosis method for multi‐agent systems

Wang

Yang

et al. 2023

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…Dynamic output feedback consensus protocol and gradient-based technique are established in [217].An observer-based, robust fault estimation method has been proposed for a nonlinear MAS [101]. Relative measurements-based fault estimation results have been presented in [218] for a leader-follower MAS. In [219], a mixed H∞/H2 optimization approach has been proposed to handle the distributed cooperative FD problem for MAS.…”

Section: Literature Reviewmentioning

confidence: 99%

Nonlinear Filtering and Reinforcement Learning-based Smart Autonomous Multi-agent Systems

Borah

2024

Preprint

View full text Add to dashboard Cite

<p>There is growing importance in complex engineering systems to operate autonomously, especially given potential malfunctions that may occur in the system, such as sensors, actuators, components, communication networks, and controllers. Fault detection, isolation, and reconstruction (FDIR) are crucial for autonomous systems. There is significant demand to evolve efficient intelligent systems to detect faults, isolate fault locations and autonomously reconstruct any component of a complex dynamical system. Hence, it is essential to detect, isolate, and reconstruct the faults efficiently and on time when the systems are in operation. This dissertation presents a novel methodology for developing smart autonomous multiagent systems (SAMAS). The SAMAS comprises several agents; some are homogeneous while others are heterogeneous. The proposed method involves developing a multi-agent systems (MAS) model and designing a decentralized smart control system. The MAS model contains homogeneous and heterogeneous agents, communicates among agents through an undirected connected graph, external disturbances, goals, and constraints. The sensor, actuator, communication, and controller faults are also modeled in the MAS model. A decentralized smart control system is designed to create a SAMAS model in the presence of uncertainty in each agents’ dynamics and faults located in the sensors, actuators, communication networks, and controllers. The proposed control method is based on non-linear filtering techniques, deep reinforcement learning, and robust control techniques. A Chebyshev neural network (CNN) is incorporated to learn the uncertain nonlinear functions in the agent dynamics of MAS. Additionally, robust control term using the hyperbolic tangent function is applied to counteract the neural network approximation errors. Meanwhile, a novel algorithm has been proposed which is employed to estimate the uncertain states of the agent dynamics and to train the internal parameters of the neural network given a set of prior measurements. Moreover, an adaptive threshold method has been proposed to detect any kinds of faults present in the system followed by a likelihood-based isolation method to locate each faulty agent. The novel algorithm is known as a reinforced unscented Kalman filter (RUKF). The primary purpose of the RUKF is to detect and isolate the faults, and to adapt the process and measurement noise covariance matrices to reconstruct the faults. To assess the performance of the proposed methodology, we developed a SAMAS consisting of six heterogeneous uncertain agent dynamics. The SAMAS model and the proposed control methodology are numerically simulated using MATLAB. A Monte-Carlo (MC) simulation was carried out to assess the performance of the proposed control methodology in the presence of uncertain agent dynamics and with the sensor, actuator, communication, and controller faults. The fault isolation results are summarized in confusion matrices for each faulty case. The stability of the RUKF, which ran in conjunction with a robust control method, has been proven using the Lyapunov stability approach. Extensive simulations were conducted to evaluate the performance of the proposed method. In this study, the proposed method showed superior performance to the standard unscented Kalman filter (UKF) and adaptive UKF (AUKF). The proposed fault isolation scheme isolated the faulty agents with over 96.68% success rate at the system level. Hence, the results of the numerical simulations show the feasibility of the proposed approach. The proposed RUKF is also computationally less expensive than the standard UKF and AUKF. The proposed approach can be considered a promising tool to evaluate fault detection, isolation, and reconstruction in complex engineering systems. Furthermore, the proposed approach can be extended to other deep space complex systems. The proposed approach can also be used for MAS problems for the financial sector, such as stock market prediction, economic time series, and multi-arm bandit problems.</p>

show abstract

“…20 The fault estimation issue was considered for MASs using distributed technology and the corresponding fault estimator was constructed. 21 However, these research results involve the actuator fault estimation, but the issue of sensor fault estimation has not been considered. The design methods of the actuator and sensor faults are completely different from each other because of the different fault locations.…”

Section: Introductionmentioning

confidence: 99%

“…A distributed strategy of fault estimation for linear MASs with the directed communication topology was presented 20 . The fault estimation issue was considered for MASs using distributed technology and the corresponding fault estimator was constructed 21 . However, these research results involve the actuator fault estimation, but the issue of sensor fault estimation has not been considered.…”

Section: Introductionmentioning

confidence: 99%

Resilient observer design of sensor fault estimation for discrete‐time multi‐agent systems: A distributed approach

Xia

Jiang

Zhang

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this article, the issue of distributed sensor fault estimation with resilient property is studied for a class of multi-agent systems. Based on the relative output information, a novel fault estimation design is presented to estimate sensor fault and state vectors simultaneously. Meanwhile, the variation of observer gain is considered to improve the robustness of fault estimation observer. Furthermore, robust l 2 − l ∞ performance is used to suppress the influence of external interference on sensor fault estimation error. Finally, simulations are provided to present effectiveness of the developed approach.

show abstract

Distributed Robust Fault Estimation Using Relative Measurements for Leader–Follower Multiagent Systems

Cited by 18 publications

References 28 publications

A new belief rule base based distributed online fault diagnosis method for multi‐agent systems

A new belief rule base based distributed online fault diagnosis method for multi‐agent systems

Nonlinear Filtering and Reinforcement Learning-based Smart Autonomous Multi-agent Systems

Resilient observer design of sensor fault estimation for discrete‐time multi‐agent systems: A distributed approach

Contact Info

Product

Resources

About