Reliable Memory Sampled-Data Consensus of Multi-Agent Systems With Nonlinear Actuator Faults

Saravanakumar, Ramasamy; Amini, Amir; Datta, Rupak; Cao, Yang

doi:10.1109/tcsii.2021.3124043

Cited by 23 publications

(20 citation statements)

References 27 publications

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“…Choosing different time delay 𝜏, the maximal sampling period h M and computational complexity computed by Corollary 1 are listed in Table 1 along with those obtained by Theorem 3 26 and Theorem 3.1. 28 The parameter in Theorem 3.1 28 are set as: 𝛼 = 1, 𝛽 = 1, K 1 = 0,  = 0.5 and actuator fault H = 1. The comparison results show that our approach is superior to that reported in Theorem 3 26 and Theorem 3.1.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…The comparison results show that our approach is superior to that reported in Theorem 3 26 and Theorem 3.1. 28 Then, by solving Corollary 1 with 𝜌 1 = 𝜌 2 = 𝜌 3 = 1 and 𝜏 = 0.5, h M = 0.3479, the gain matrix K of the sampled-data controller can be obtained:…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…Then, a memory-based sampled-data consensus framework was proposed for general linear MASs with actuator faults based on a looped functional approach. 28 On the one hand, as pointed out in Reference 29, a nonfragile sampled-data controller can be designed to guarantee the maximal allowable upper bound of the sampling intervals by solving the effective linear matrix inequality (LMI). On the other hand, on the basis of our previous work, 30 the nonfragile consensus condition for MASs was improved by employing a quadratic polynomial Lyapunov-Krasovskii functional.…”

Section: Introductionmentioning

confidence: 99%

“…In place of classical periodic sampled‐data controllers, References 26 and 27 considered the aperiodic sampled‐data consensus control problems of nonlinear MASs by using the input delay approach and the free‐weighting matrix method. Then, a memory‐based sampled‐data consensus framework was proposed for general linear MASs with actuator faults based on a looped functional approach 28 . On the one hand, as pointed out in Reference 29, a nonfragile sampled‐data controller can be designed to guarantee the maximal allowable upper bound of the sampling intervals by solving the effective linear matrix inequality (LMI).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Aperiodic sampled‐data consensus control for homogeneous and heterogeneous multi‐agent systems: A looped‐functional method

Peng

2023

Intl J Robust & Nonlinear

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This paper examines the aperiodic sampled-data consensus control problems for nonlinear homogeneous and heterogeneous multi-agent systems (MASs). For homogeneous MASs, the consensus conditions, with and without time delays are derived by using a two-sided-looped functional. This less conservative approach takes full account of the information on the intervals 𝛿(t) to 𝛿(t k ) and 𝛿(t) to 𝛿(t k+1 ). On this basis, two aperiodic sampled-data controllers are designed, and additional results on the estimate for the upper bound of the maximum allowable sampling interval are given as well. For heterogeneous MASs, the bounded consensus tracking criterion is derived based on the improved looped functional method. In addition, a corresponding heterogeneous sampled-data controller is designed for each follower to ensure that the consensus tracking error exponentially converges to a bounded ellipsoid region. Finally, three illustrative examples are provided to show the efficiency and superiority of the proposed results.

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Section: Numerical Simulationsmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Aperiodic sampled‐data consensus control for homogeneous and heterogeneous multi‐agent systems: A looped‐functional method

Peng

2023

Intl J Robust & Nonlinear

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“…Because of this, it is crucial to implement consensus algorithms in multi-agent systems based on the sampled-data control method. A typical example is the sampled-data system [3,14], where https://www.journals.vu.lt/nonlinear-analysis the sampled data are used during sampling intervals and updated at sampling instants. Whereafter, the distributed consensus optimization problem of multi-agent systems with delayed sampled-data is considered in [20].…”

Section: Introductionmentioning

confidence: 99%

Distributed optimization for multi-agent systems with communication delays and external disturbances under a directed network

Yu²,

Huang³

et al. 2023

NAMC

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This article studies the distributed optimization problem for multi-agent systems with communication delays and external disturbances in a directed network. Firstly, a distributed optimization algorithm is proposed based on the internal model principle in which the internal model term can effectively compensate for external environmental disturbances. Secondly, the relationship between the optimal solution and the equilibrium point of the system is discussed through the properties of the Laplacian matrix and graph theory. Some sufficient conditions are derived by using the Lyapunov–Razumikhin theory, which ensures all agents asymptotically reach the optimal value of the distributed optimization problem. Moreover, an aperiodic sampled-data control protocol is proposed, which can be well transformed into the proposed time-varying delay protocol and analyzed by using the Lyapunov–Razumikhin theory. Finally, an example is given to verify the effectiveness of the results.

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Adaptive finite‐time fuzzy secure control for nonlinear MASs under deception attacks

Bai,

Bi,

Sui

et al. 2024

Intl J Robust & Nonlinear

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SummaryIn this paper, the problem of adaptive security control for a class of nonlinear multi‐agent systems (MASs) under deception attacks is studied. This scheme aims to design a new adaptive fuzzy control scheme for each subsystem of MASs to reduce the impact of network attacks (from sensor to controller) on state signals, and proposes Nussbaum function to deal with unknown time‐varying control directions. In addition, for multi‐agent systems, a finite‐time convergent security control scheme is designed to ensure the convergence of tracking error and the boundedness of signals in the closed‐loop system. Finally, the effectiveness of the proposed method is verified by a simulation example.

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Reliable Memory Sampled-Data Consensus of Multi-Agent Systems With Nonlinear Actuator Faults

Cited by 23 publications

References 27 publications

Aperiodic sampled‐data consensus control for homogeneous and heterogeneous multi‐agent systems: A looped‐functional method

Aperiodic sampled‐data consensus control for homogeneous and heterogeneous multi‐agent systems: A looped‐functional method

Distributed optimization for multi-agent systems with communication delays and external disturbances under a directed network

Adaptive finite‐time fuzzy secure control for nonlinear MASs under deception attacks

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