Literature DB >> 31180878

Output-Feedback Cooperative Formation Maneuvering of Autonomous Surface Vehicles With Connectivity Preservation and Collision Avoidance.

Zhouhua Peng, Dan Wang, Tieshan Li, Min Han.   

Abstract

In this paper, a cooperative time-varying formation maneuvering problem with connectivity preservation and collision avoidance is investigated for a fleet of autonomous surface vehicles (ASVs) with position-heading measurements. Each vehicle is subject to unknown kinetics induced by internal model uncertainty and external disturbances. At first, a nonlinear state observer is used to recover the unmeasured linear velocity and yaw rate as well as unknown uncertainty and disturbances. Then, observer-based cooperative time-varying formation maneuvering control laws are designed based on artificial potential functions, nonlinear tracking differentiators, and a backstepping technique. The stability of closed-loop distributed formation control system is analyzed based on input-to-state stability and cascade stability. The salient features of the proposed method are as follows. First, cooperative time-varying formation maneuvering with the capability of connectivity preservation and collision avoidance can be achieved in the absence of velocity measurements. Second, the complexity of the cooperative time-varying formation maneuvering control laws is reduced without resorting to dynamic surface control. Third, the uncertainty and disturbance are actively rejected in the presence of position-heading measurements. Simulation results are given to substantiate the proposed output feedback control method for cooperative time-varying formation maneuvering of ASVs with connectivity preservation and collision avoidance.

Year:  2019        PMID: 31180878     DOI: 10.1109/TCYB.2019.2914717

Source DB:  PubMed          Journal:  IEEE Trans Cybern        ISSN: 2168-2267            Impact factor:   11.448


  3 in total

1.  Intelligent L2-L∞ Consensus of Multiagent Systems under Switching Topologies via Fuzzy Deep Q Learning.

Authors:  Haoyu Cheng; Linpeng Xu; Ruijia Song; Yue Zhu; Yangwang Fang
Journal:  Comput Intell Neurosci       Date:  2022-02-16

2.  On the Problem of Restoring and Classifying a 3D Object in Creating a Simulator of a Realistic Urban Environment.

Authors:  Mikhail Gorodnichev; Sergey Erokhin; Ksenia Polyantseva; Marina Moseva
Journal:  Sensors (Basel)       Date:  2022-07-12       Impact factor: 3.847

3.  Distributed model-free formation control of networked fully-actuated autonomous surface vehicles.

Authors:  Xiaobing Niu; Shengnan Gao; Zhibin Xu; Shiliang Feng
Journal:  Front Neurorobot       Date:  2022-09-29       Impact factor: 3.493

  3 in total

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