The adaptive prescribed-time (PT) formation control is addressed in this paper for multiple unmanned surface vehicles (USVs) with parameter uncertainties and external disturbances. The novelty lies in proposing a novel dynamic surface control (DSC)-based PT formation algorithm. Specifically, to effectively compensate for filter errors and facilitate PT convergence, a new nonlinear filter (NLF) with an adaptive parameter estimator and a piece-wise function is first constructed within the DSC framework. Subsequently, combined with the adaptive technology, a unified PT control scheme is provided. It can ensure the achievement of expected formation pattern within a predefined time, while guaranteeing that formation errors converge to a user-defined set. More importantly, the proposed control framework not only tackles the complexity explosion caused by conventional backstepping but also reduces the constraints on filter design parameters. Finally, the presented scheme’s validity is confirmed through simulation implementation.

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Adaptive Prescribed-Time Formation Control of Surface Vehicles Using Dynamic Surface Method

  • Ping Wang,
  • Chengpu Yu,
  • Maolong Lv

摘要

The adaptive prescribed-time (PT) formation control is addressed in this paper for multiple unmanned surface vehicles (USVs) with parameter uncertainties and external disturbances. The novelty lies in proposing a novel dynamic surface control (DSC)-based PT formation algorithm. Specifically, to effectively compensate for filter errors and facilitate PT convergence, a new nonlinear filter (NLF) with an adaptive parameter estimator and a piece-wise function is first constructed within the DSC framework. Subsequently, combined with the adaptive technology, a unified PT control scheme is provided. It can ensure the achievement of expected formation pattern within a predefined time, while guaranteeing that formation errors converge to a user-defined set. More importantly, the proposed control framework not only tackles the complexity explosion caused by conventional backstepping but also reduces the constraints on filter design parameters. Finally, the presented scheme’s validity is confirmed through simulation implementation.