Output-Feedback Flocking Control of Multiple Autonomous Surface Vehicles Based on Data-Driven Adaptive Extended State Observers

• Published in: IEEE transactions on cybernetics Vol. 51; no. 9; pp. 4611 - 4622
• Main Authors: Peng, Zhouhua, Liu, Lu, Wang, Jun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Autonomous surface vehicles (ASVs); Collision avoidance; Control methods; Control theory; Cybernetics; data-driven adaptive extended state observer (ESO); disturbances; flocking; Mathematical models; Observers; Output feedback; Parameterization; Parameters; Sea surface; State observers; Surface vehicles; Task analysis; unknown control gains; Urban areas; Velocity measurement
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2020.3009992

This article addresses an output-feedback flocking control problem for a swarm of autonomous surface vehicles (ASVs) to follow a leading ASV guided via a parameterized path. The leading and following ASVs are subject to completely unknown model parameters, external disturbances, and unmeasured velocities. A data-driven adaptive anti-disturbance control method is proposed for establishing a flocking behavior without any prior knowledge of model parameters. Specifically, a data-driven adaptive extended state observer (ESO) is proposed such that unknown input gains, unmeasured velocities, and total disturbance are simultaneously estimated. For the leading ASV, an output-feedback path-following control law is developed to follow a predefined parameterized path. For following ASVs, an output-feedback flocking control law is developed based on an artificial potential function for collision avoidance and connectivity preservation, in addition to a distributed ESO for estimating the velocity of the leading ASV through a cooperative estimation network. The simulation results are discussed to substantiate the efficacy of the proposed path-guided output-feedback ASV flocking control based on data-driven adaptive ESOs without measured velocity information.