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Path-guided time-varying formation control with collision avoidance and connectivity preservation of under-actuated autonomous surface vehicles subject to unknown input gains

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Abstract This paper investigates the distributed time-varying formation control for a swarm of under-actuated autonomous surface vehicles subject to unknown input gains, in addition to model uncertainties and ocean disturbances.… Click to show full abstract

Abstract This paper investigates the distributed time-varying formation control for a swarm of under-actuated autonomous surface vehicles subject to unknown input gains, in addition to model uncertainties and ocean disturbances. The fleet is required to follow a parameterized path with a time-varying formation while avoiding collisions and maintaining connectivity at a complex sea environment. At the kinematic level, a distributed guidance control law is proposed based on a consensus approach, a path-following design, artificial potential functions, and an auxiliary variable approach. At the kinetic level, an adaptive kinetic control law is designed based on an indirect model reference adaptive control approach where a neural estimator is developed for identifying unknown input gains, model uncertainties and ocean disturbances. The stability of the closed-loop system is proven via cascade stability analysis. The advantage of the proposed method is three-folds. First, the developed time-varying formation controller is able to achieve various coordinated behaviors such as cooperative target tracking and target enclosing. Second, the proposed time-varying formation controller is robust against model uncertainties, ocean disturbances and unknown input gains. Third, the proposed distributed controller holds the capability of collision avoidance and connectivity preservation. Simulation results substantiate the effectiveness of the proposed path-guided time-varying formation controllers.

Keywords: time varying; control; varying formation; unknown input

Journal Title: Ocean Engineering
Year Published: 2019

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