Adaptive Fault-Tolerant Output Regulation of Linear Multi-Agent Systems With Sensor Faults

Sun, Yi-Ran; Xia, Yunxia; Zhang, Jie; Ding, Dawei

doi:10.1109/access.2020.3020367

Cited by 15 publications

(5 citation statements)

References 19 publications

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“…Secondly, as reviewed in the literature, a good number of present works consider a team of agents where each agent has a linear time-invariant (LTI) dynamical representation [210,220], whereas most of the practical systems are nonlinear. The major emphasis has been put on the estimation of sensor/actuator faults, but the detection and isolation problem has been neglected in several works [214,[221][222][223]. Hence, there are still many open challenges in this area.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Borah

2024

Preprint

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<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>

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Section: Literature Reviewmentioning

confidence: 99%

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

Borah

2024

Preprint

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“…A method of diagnosing distributed sensor faults with L 2 − L ∞ performance is explored in [34]. An algorithm for fault-tolerant output regulation is proposed for leader-follower MASs in the presence of sensor faults in [35], which guarantees convergence of all output tracking errors. Furthermore, a distributed FTC algorithm based on sliding mode and radial basis function neural networks is studied in [36].…”

Section: Introductionmentioning

confidence: 99%

Distributed Adaptive Fault-Tolerant Control for Leaderless/Leader–Follower Multi-Agent Systems against Actuator and Sensor Faults

Cheng

et al. 2023

Electronics

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The faults of actuators and sensors can lead to abnormal operations or even system faults in multi-agent systems (MASs). To address this issue, this paper proposes an adaptive fault-tolerant control (FTC) algorithm for leaderless/leader–follower MASs against actuator and sensor faults. First, extended states integrating the fault components are constructed and the MAS is transformed into a descriptor system form. Then, a sliding-mode observer is designed for the transformed MAS. Based on the estimated MAS states and faults, adaptive FTC algorithms are developed, which update the control gains with the distributed tracking error. Finally, numerical simulations demonstrate that the proposed method can guarantee MAS stability against actuator and sensor faults.

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“…Note that the adversaries do not need any information about the dynamical systems to perform this type of attack on the sparse and distributed communication networks, which could undermine the cooperative performance and even destabilize the entire networked MAS. Fault-tolerant control has been widely studied for MAS to deal with faults [28][29][30][31][32]. Time-varying formation control problem for second-order MAS subject to actuator failures is studied in ref.…”

Section: Introductionmentioning

confidence: 99%

“…[28]. Various remedies for sensor faults are provided through adaptive compensator [29] and distributed fault estimation [31]. A fully adaptive containment controller for homogeneous MAS with bounded actuator bias faults is developed in ref.…”

Section: Introductionmentioning

confidence: 99%

Resilience‐based output containment control of heterogeneous MAS against unbounded attacks

Sun

Zhang

Yang

et al. 2022

IET Control Theory & Appl

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This paper studies the resilience-based output containment control problem of heterogeneous multi-agent systems in the presence of simultaneous false data injection attacks on both sensors and actuators injected on both cyber and physical layers. In real-world security problems, malicious adversaries could inject false data injection attacks on both sensors and actuators simultaneously, even with unbounded injections, to maximize their damage to the multi-agent systems. To address such a problem, a distributed observer is first designed for each follower to estimate the actual system states against unbounded sensor attacks. A resilience-based control framework is then proposed using state estimation and adaptive control techniques to compensate for the bounded actuator attack signals adaptively. Lyapunov stability analysis shows that uniformly ultimately bounded convergence is achieved despite the simultaneously injected unbounded sensor attacks and bounded actuator attacks on both cyber and physical layers. That is, each follower's output trajectory converges to a small neighbourhood around the convex hull spanned by the leaders' output trajectories. The effectiveness of the proposed theoretical results is verified by case studies using numerical simulations.

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Adaptive Fault-Tolerant Output Regulation of Linear Multi-Agent Systems With Sensor Faults

Cited by 15 publications

References 19 publications

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

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

Distributed Adaptive Fault-Tolerant Control for Leaderless/Leader–Follower Multi-Agent Systems against Actuator and Sensor Faults

Resilience‐based output containment control of heterogeneous MAS against unbounded attacks

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