Adaptive Neural Consensus of Unknown Non-Linear Multi-Agent Systems with Communication Noises under Markov Switching Topologies

Guo, Shaoyan; Xie, Longhan

doi:10.3390/math12010133

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…Consensus algorithms [1] have been studied as powerful tools for distributed networks [2] in multiple applications [3] such as control [4], estimation [5] and the internet of vehicles [6]. They are based on a graph that describes the interconnections of the agents in the network, and the goal of the network is to achieve agreement upon some data, policy or action.…”

Section: Introductionmentioning

confidence: 99%

Distributed Consensus for Global Matrix Formation in the Principal Component Pursuit Scenario

Suárez,

Velásquez

2024

Applied Sciences

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The aim behind principal component pursuit is to recover a low-rank matrix and a sparse matrix from a noisy signal which is the sum of both matrices. This optimization problem is a priori and non-convex and is useful in signal processing, data compression, image processing, machine learning, fluid dynamics, and more. Here, a distributed scheme described by a static undirected graph, where each agent only observes part of the noisy or corrupted matrix, is applied to achieve a consensus; then, a robust approach that can also handle missing values is applied using alternating directions to solve the convex relaxation problem, which actually solves the non-convex problem under some weak assumptions. Some examples of image recovery are shown, where the network of agents achieves consensus exponentially fast.

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Section: Introductionmentioning

confidence: 99%

Distributed Consensus for Global Matrix Formation in the Principal Component Pursuit Scenario

Suárez,

Velásquez

2024

Applied Sciences

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“…It is noteworthy that, in reality, systems frequently encounter nonlinear dynamics, sensor noise, non-ideal environmental variables, etc. Therefore, most practical multi-agent systems are nonlinear [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Leader-Following Consensus of Discrete-Time Nonlinear Multi-Agent Systems with Asymmetric Saturation Impulsive Control

Yuan,

Chen,

Tian

et al. 2024

Mathematics

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Impulsive control is an effective approach for coordinating multi-agent systems in practical environments due to its high robustness and low cost. However, impulsive control exhibits characteristics such as high amplitude and rapid variation, potentially presenting threats to the equipment. Additionally, multi-agent systems are constrained by input saturation due to limitations in physical controller structures and information-processing capabilities. These saturation constraints may be asymmetrical. Therefore, it is necessary to consider the saturation constraint when implementing impulsive control, as it can also mitigate the threats posed by the impulse to agents. This paper investigates the leader-following consensus for a class of discrete-time nonlinear multi-agent systems, proposing an asymmetric saturation impulsive control protocol to reduce the energy consumption and damage to the equipment. Regarding the handle of asymmetric saturation, an approach is proposed that eliminates the need for transformation from the asymmetric case to the symmetric case, which retains the saturation function and directly introduces the sector condition to deal with saturation nonlinearity. Furthermore, based on Lyapunov stability theory and matrix theory, sufficient conditions for leader-following consensus in discrete-time nonlinear multi-agent systems under asymmetric saturation impulsive control are established, and the admissible region of the system is estimated. Finally, numerical simulations are provided to verify the validity of the theoretical results.

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Fixed‐time formation control of second‐order nonlinear multi‐agent systems using the neural network dynamic sliding mode

Luo,

Huang,

Cao

et al. 2024

Intl J Robust & Nonlinear

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The problem of fixed‐time formation control for a class of second‐order nonlinear multi‐agent systems is studied. For a class of such systems, a control algorithm is proposed to maintain the connections among the agents while avoiding collisions. Furthermore, a radial basis function neural network is used in the design to precisely approximate the nonlinear function for the nonlinear terms in the model. Then, a dynamic sliding mode control method is proposed to suppress the chattering phenomenon that may arise due to the sliding mode control. A sufficient condition for the system to achieve fixed‐time formation is obtained by using different methods, such as Lyapunov stability. Finally, the effectiveness of the proposed algorithm is verified by example. Simulation experiments reveal that the proposed method has faster error convergence and better robust control than conventional algorithms.

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Adaptive Neural Consensus of Unknown Non-Linear Multi-Agent Systems with Communication Noises under Markov Switching Topologies

Cited by 3 publications

References 48 publications

Distributed Consensus for Global Matrix Formation in the Principal Component Pursuit Scenario

Distributed Consensus for Global Matrix Formation in the Principal Component Pursuit Scenario

Leader-Following Consensus of Discrete-Time Nonlinear Multi-Agent Systems with Asymmetric Saturation Impulsive Control

Fixed‐time formation control of second‐order nonlinear multi‐agent systems using the neural network dynamic sliding mode

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