Distributed optimization of networked marine surface vehicles: A fixed-time estimator-based approach

Chen, Qian; Ge, Ming‐Feng; Liang, Chang‐Duo; Gu, Zhi-Wei; Liu, Jie

doi:10.1016/j.oceaneng.2023.115275

Cited by 10 publications

(2 citation statements)

References 32 publications

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“…Finally, many concepts and methodologies employed in the proposed results, such as the reinforcement learning framework and the simulation environment, can be adapted for Networked Marine Surface Vehicles (NMSVs) [32,33], since the the underlying principles of autonomous navigation and adaptation to environmental disturbances in the case of individual USVs remain relevant. For this purpose, we can extend the proposed framework in building collaborative multi-agent systems with sharing data and coordinating actions to achieve common objectives [34,35]. Also, we can possibly consider issues about scalability and adaptability for operating in dynamic marine environments, exploring the capability of NMSVs to dynamically adjust their behavior and strategies based on real-time information exchange within the network.…”

Section: Discussionmentioning

confidence: 99%

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

Chaysri,

Spatharis,

Vlachos

et al. 2024

Sensors

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This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained.

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Section: Discussionmentioning

confidence: 99%

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

Chaysri,

Spatharis,

Vlachos

et al. 2024

Sensors

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“…Recently, distributed optimization problem (DOP) of MASs has attracted much interest since its wide range applications including robotic systems [1,2], sensor networks [3], marine surface vehicles [4], smart grids [5,6], fractional order MASs [7] and multiple one-link manipulators system [8]. Given a DOP, each agent acquires a local objective function and the MASs have a global objective function obtained by making the sum of local objective functions.…”

Section: Introductionmentioning

confidence: 99%

Distributed Adaptive Optimization Algorithm for Fractional High-Order Multiagent Systems Based on Event-Triggered Strategy and Input Quantization

Yang,

Yuan,

Chen

et al. 2023

Fractal Fract

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This paper investigates the distributed optimization problem (DOP) for fractional high-order nonstrict-feedback multiagent systems (MASs) where each agent is multiple-input–multiple-output (MIMO) dynamic and contains uncertain dynamics. Based on the penalty-function method, the consensus constraint is eliminated and the global objective function is reconstructed. Different from the existing literatures, where the DOPs are addressed for linear MASs, this paper deals with the DOP through using radial basis function neural networks (RBFNNs) to approximate the unknown nonlinear functions for high-order MASs. To reduce transmitting and computational costs, event-triggered scheme and quantized control technology are combined to propose an adaptive backstepping neural network (NN) control protocol. By applying the Lyapunov stability theory, the optimal consensus error is proved to be bounded and all signals remain semi-global uniformly ultimately bounded. Simulation shows that all agents reach consensus and errors between agents’ outputs and the optimal solution is close to zero with low computational costs.

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Prescribed-Time Leader-Follower Bipartite Consensus for Nonlinear Multi-agent Systems

Chen,

Yu,

et al. 2024

Communications in Computer and Information Science

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Distributed optimization of networked marine surface vehicles: A fixed-time estimator-based approach

Cited by 10 publications

References 32 publications

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

Distributed Adaptive Optimization Algorithm for Fractional High-Order Multiagent Systems Based on Event-Triggered Strategy and Input Quantization

Prescribed-Time Leader-Follower Bipartite Consensus for Nonlinear Multi-agent Systems

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