Distributed formation control of fractional-order multi-agent systems with relative damping and nonuniform time-delays

Liu, Jun; Li, Ping; Chen, Wei; Qin, Kaiyu; Qi, Lei

doi:10.1016/j.isatra.2019.03.012

Cited by 28 publications

(6 citation statements)

References 20 publications

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“…The CommNet acquiesces agents use the full connection mechanism within a certain range to share information, using N (j) to represent the set of agents j. Hidden state h i j and exchange information c i j are used to get the exchange information of next moment, the calculation of h i j and c i j are as ( 6) and (7).…”

Section: Commnetmentioning

confidence: 99%

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BiC-DDPG: Bidirectionally-Coordinated Nets for Deep Multi-agent Reinforcement Learning

Wang¹,

Shi²,

Xue³

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Multi-agent reinforcement learning (MARL) often faces the problem of policy learning under large action space. There are two reasons for the complex action space: first, the decision space of a single agent in a multi-agent system is huge. Second, the complexity of the joint action space caused by the combination of the action spaces of different agents increases exponentially from the increase in the number of agents. How to learn a robust policy in multi-agent cooperative scenarios is a challenge. To address this challenge we propose an algorithm called bidirectionally-coordinated Deep Deterministic Policy Gradient (BiC-DDPG). In BiC-DDPG three mechanisms were designed based on our insights against the challenge: we used a centralized training and decentralized execution architecture to ensure Markov property and thus ensure the convergence of the algorithm, then we used bi-directional rnn structures to achieve information communication when agents cooperate, finally we used a mapping method to map the continuous joint action space output to the discrete joint action space to solve the problem of agents' decision-making on large joint action space. A series of fine grained experiments in which include scenarios with cooperative and adversarial relationships between homogeneous agents were designed to evaluate our algorithm. The experiment results show that our algorithm out performing the baseline.

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Section: Commnetmentioning

confidence: 99%

“…In particular, several league pools are designed to continuously learn better policies and countermeasures. The multi-agent reinforcement learning algorithms also performs well in autonomous cars [7] and the coordination of robot swarms [8].…”

Section: Introductionmentioning

confidence: 99%

BiC-DDPG: Bidirectionally-Coordinated Nets for Deep Multi-agent Reinforcement Learning

Wang¹,

Shi²,

Xue³

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Recently, the exploration of formation control in fractional systems has garnered considerable interest due to their capacity to capture intricate dynamics with memory and long-range dependencies [11,12]. Fractional systems, characterized by non-integer derivatives, have demonstrated promising applications across diverse domains, including control systems [13], signal processing [14], and optimization. By extending fractional dynamics to multi-agent systems, the formation control challenge in fractional multi-agent systems has emerged as a captivating and sought-after research area.…”

Section: Introductionmentioning

confidence: 99%

Leader-Following Formation Control for Discrete-Time Fractional Stochastic Multi-Agent Systems by Event-Triggered Strategy

Wu,

Yu,

Ren

2024

Fractal Fract

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Fractional differential equations, which are non-local and can better describe memory and genetic properties, are widely used to describe various physical, chemical, and biological phenomena. Therefore, the multi-agent systems based on discrete-time fractional stochastic models are established. First, some followers are selected for pinning control. In order to save resources and energy, an event-triggered based control mechanism is proposed. Second, under this control mechanism, sufficient conditions on the interaction graph and the fractional derivative order such that formation control can be achieved are given. Additionally, influenced by noise, the multi-agent system completes formation control in the mean square. In addition to that, these results are equally applicable to the discrete-time fractional formation problem without noise. Finally, the example of numerical simulation is given to prove the correctness of the results.

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“…So such a medication can strengthen the ability of neurons to process information. Fortunately, owing to the unwavering tenacity of researchers, various worldwide applications of FONNs have been discovered, including network approximation [24], state estimation [25], system identification [26], robotic manipulators [27], and formation control [28]. For neurons, fractional-order elements have two clear benefits.…”

Section: Introductionmentioning

confidence: 99%

A Study on the 3D Hopfield Neural Network Model via Nonlocal Atangana–Baleanu Operators

et al. 2022

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Hopfield neural network (HNN) is considered as an artificial model derived from the brain structures and it is an important model that admits an adequate performance in neurocomputing. In this article, we solve a dynamical model of 3D HNNs via Atangana–Baleanu (AB) fractional derivatives. To find the numerical solution of the considered dynamical model, the well-known Predictor-Corrector (PC) method is used. A number of cases are taken by using two different sets of values of the activation gradient of the neurons as well as six different initial conditions. The given results have been perfectly established using the different fractional-order values on the given derivative operator. The objective of this research is to investigate the dynamics of the proposed HNN model at various values of fractional orders. Nonlocal characteristic of the AB derivative contains the memory in the system which is the main motivation behind the proposal of this research.

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Distributed formation control of fractional-order multi-agent systems with relative damping and nonuniform time-delays

Cited by 28 publications

References 20 publications

BiC-DDPG: Bidirectionally-Coordinated Nets for Deep Multi-agent Reinforcement Learning

BiC-DDPG: Bidirectionally-Coordinated Nets for Deep Multi-agent Reinforcement Learning

Leader-Following Formation Control for Discrete-Time Fractional Stochastic Multi-Agent Systems by Event-Triggered Strategy

A Study on the 3D Hopfield Neural Network Model via Nonlocal Atangana–Baleanu Operators

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