Consensus in second-order multiple flying vehicles with random delays governed by a Markov chain

Mu, Bingxian; Li, Huxiong; Ding, Jie; Shi, Yang

doi:10.1016/j.jfranklin.2015.01.034

Cited by 32 publications

(8 citation statements)

References 24 publications

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“…Those mentioned features seemingly make the consensus based method more practical and beneficial for real-word applications among various formation control methods. While such a method has attracted much attention in the studies of groups of unmanned ground vehicles [88][89][90][91][92] , unmanned aerial vehicles [93][94][95][96][97][98] , and spacecraft [99][100][101][102] , there is still a lack of sufficient research efforts to apply consensus based approach to the multi-AUV formation where more practical challenges associated should be addressed further, most of which will be discussed in what follows.…”

Section: Other Approachesmentioning

confidence: 99%

Formation control of multiple autonomous underwater vehicles: a review

Yan¹,

Xu²,

Yang³

et al. 2023

Intell Robot

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This paper presents a comprehensive overview of recent developments in formation control of multiple autonomous underwater vehicles (AUVs). Several commonly used structures and approaches for formation coordination are listed, and the advantages and deficiencies of each method are discussed. The difficulties confronted in synthesis of a practical AUVs formation system are clarified and analyzed in terms of the characteristic of AUVs, adverse underwater environments, and communication constraints. The state-of-the-art solutions available for addressing these challenges are reviewed comprehensively. Based on that, a brief discussion is made, and a list of promising future work is pointed out, which aims to be helpful for the further promotion of AUVs formation applications.

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Section: Other Approachesmentioning

confidence: 99%

Formation control of multiple autonomous underwater vehicles: a review

Yan¹,

Xu²,

Yang³

et al. 2023

Intell Robot

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“…Compared with a single quadrotor, the quadrotor formation may provide a better performance for the difficult tasks [6]- [10]. Furthermore, system stability and reliability can also be improved through the exchange of information among multiple quadrotors.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Super-Twisting Distributed Formation Control of Multi-Quadrotor Under External Disturbance

Zhang

Chen

Shang³

et al. 2021

IEEE Access

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In this paper, the adaptive super-twisting distributed formation control of multi-quadrotor in the presence of external disturbances and uncertainties is studied. First, the quadrotor formation system is separated into a position subsystem and an attitude subsystem which are represented by unit-quaternions. And then a composite adaptive super-twisting control method is proposed for the position subsystem and attitude subsystem respectively. For the position subsystem, an adaptive multivariable super-twisting controller is designed such that the positions of formation converge to the desired formation configuration and generate the desired attitude. And the adaptive fast super-twisting controller is designed for the attitude subsystem to track the desired attitude in finite time. Based on Lyapunov-based stability analysis, finite time convergence stability of the whole closed-loop system is proved. Finally, a numerical simulation result is provided to illustrate the effectiveness of the proposed formation control scheme.

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“…Besides the first-order multi-agent system, the consensus problem of MASs with more complicated dynamics is also studied. In [9], multiple flying vehicles were modeled as second-order systems and the consensus problem was discussed. The consensus problem of high-order MASs with undirected networks was discussed in [10].…”

Section: Introductionmentioning

confidence: 99%

Robust Optimal Formation Control of Heterogeneous Multi-Agent System via Reinforcement Learning

2020

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In this paper, a distributed robust optimal formation control problem is studied based on reinforcement learning for the heterogeneous multi-agent system with partial unknown system parameters. The formation system is subjected to equivalent disturbances containing parameter uncertainties and external disturbances. The proposed robust optimal controller consists of a nominal controller and a robust compensator. For the nominal controller, the reinforcement learning algorithm is proposed to obtain the optimal control input. For the robust compensator, the reinforcement learning algorithm is firstly used to identify the unknown dynamic parameters and then the robust compensator is designed to restrain the equivalent disturbances in the formation system. The robustness properties of the global multi-agent system are proven. A simulation of heterogeneous rotorcrafts is provided to verify the effectiveness of the proposed method. INDEX TERMS Formation control, reinforcement learning, robust optimal control, heterogeneous multiagent systems.

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Consensus in second-order multiple flying vehicles with random delays governed by a Markov chain

Cited by 32 publications

References 24 publications

Formation control of multiple autonomous underwater vehicles: a review

Formation control of multiple autonomous underwater vehicles: a review

Adaptive Super-Twisting Distributed Formation Control of Multi-Quadrotor Under External Disturbance

Robust Optimal Formation Control of Heterogeneous Multi-Agent System via Reinforcement Learning

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