Lyapunov-Based Control of a Nonlinear Multiagent System With a Time-Varying Input Delay Under False-Data-Injection Attacks

Sargolzaei, Arman; Allen, Brendon C.; Crane, Carl D.; Dixon, Warren E.

doi:10.1109/tii.2021.3106009

Cited by 39 publications

(14 citation statements)

References 45 publications

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“…Finally, we demonstrated that the proposed control algorithm is valid through a numerical example and an example with two one‐link systems. In a future study, we intend to investigate how the adaptive fixed‐time control method can be extended to switched interconnected nonlinear systems with false‐data‐injection attacks 44‐48 …”

Section: Discussionmentioning

confidence: 99%

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Decentralized adaptive neural fixed‐time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

Hao

Pan

Zhu

et al. 2022

Intl J Robust & Nonlinear

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This article devises a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems containing partially unmeasurable states and time-varying output constraints. Radial basis function neural networks, as function approximators, are utilized to model the unknown functions, and the partially unmeasurable states of the systems are estimated by a reduced-order observer. By constructing a transferred function, system outputs are directly constrained in a time-varying constraint bound. Meanwhile, the first-order sliding mode differentiators are utilized to reduce the computational burden caused by the repeated differentiations of virtual controllers. Under the Lyapunov function and the fixed-time theory, the decentralized adaptive fixed-time controllers are constructed. It is proved that the closed-loop systems are fixed-time stable and the output signals are restricted in the bounded compact set. Finally, two simulation examples demonstrate the validity of the proposed control scheme.

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Section: Discussionmentioning

confidence: 99%

“…In a future study, we intend to investigate how the adaptive fixed-time control method can be extended to switched interconnected nonlinear systems with false-data-injection attacks. [44][45][46][47][48]…”

Section: Discussionmentioning

confidence: 99%

Decentralized adaptive neural fixed‐time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

Hao

Pan

Zhu

et al. 2022

Intl J Robust & Nonlinear

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“…He et al [12] survey malicious attacks in MAS and report those considered in this paper, i.e., deception attacks including false data injection. These attacks are analyzed in Sargolzaei et al [36] with the goal of detecting their occurrence, however the impact of these attacks on the system performance is not considered. The trade-off between performance and security is analyzed in [31] and privacy mechanisms are considered as overheads that slow down the system.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Fault-Tolerant Multiagent Coordination Mechanisms

Pinciroli

Trubiani

2023

IEEE Trans. Ind. Inf.

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Performance evaluation of multi-agent systems (MAS) embraces several challenges due to uncertain operational environments, such as software/hardware failures and unfaithful communications that facilitate the spread of deceptive messages. One way to smooth the impact of reliability and security potential issues in MAS is to enforce different coordination mechanisms among agents (i.e., coordinating multiple agents that need to perform a sequence of actions to maximize a system-level reward), and evaluate their efficiency. In this paper, we propose a tool-based approach, namely COORDINATE, that simulates and compares the performance characteristics of different coordination mechanisms for MAS while considering faulttolerant and corrupted agents. A Smart Hospital is adopted as illustrative example to show the need of performancebased analysis results pointing out which coordination mechanism is more efficient. Experimental results indicate that the inter-arrival time of tasks to be accomplished, the failure probability of agents, and the ratio of faithful to malicious agents contribute to determining the efficiency of different coordination mechanisms. When varying these parameters in the considered scenarios, the system latency can be reduced up to 4.2× by selecting the optimal coordination mechanism. This way, software developers are informed on the system peculiarities that trigger the switching among such coordination mechanisms for a performance-based optimal solution.

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“…Euler-Lagrange systems (ELSs) are widely applied as the universal model for that it can describe a large class physical systems, such as robot manipulators [1,2], quadrotor helicopters [3,4], and spacecraft attitude control system [5,6]. In recent decades, plenty of fruitful control schemes have been proposed; see in previous studies [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Predefined‐time control for Euler–Lagrange systems with model uncertainties and actuator faults

Wang

Hou

Feng

et al. 2022

Asian Journal of Control

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A novel adaptive predefined-time tracking control algorithm is proposed for the Euler-Lagrange systems (ELSs) with model uncertainties and actuator faults. Compared with traditional finite-time and fixed-time studies, the system output tracking error under the proposed predefined-time controller converges to a small neighborhood of zero in finite time, whose upper bound is exactly a design parameter in the control algorithm. For the uncertain model, radial-based function neural network (RBFNN) is utilized to approximate the continuous uncertain dynamics. To deal with the actuator faults, an adaptive control law is involved in the fault-tolerant controller. In order to achieve the predefined-time bounded, a novel predefined-time sliding mode surface is designed. It is proved that the tracking error vector trajectory of closed-loop system is semi-globally uniformly ultimately predefined-time bounded, and the upper bounds of both the system settling time and the corresponding output tracking error can be adjusted with a simple parameter. Simulation examples finally demonstrate the effectiveness of the proposed control algorithm.

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Lyapunov-Based Control of a Nonlinear Multiagent System With a Time-Varying Input Delay Under False-Data-Injection Attacks

Cited by 39 publications

References 45 publications

Decentralized adaptive neural fixed‐time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

Decentralized adaptive neural fixed‐time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

Performance Analysis of Fault-Tolerant Multiagent Coordination Mechanisms

Predefined‐time control for Euler–Lagrange systems with model uncertainties and actuator faults

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