What does this research mean for the field?

The proposed algorithms enable underactuated autonomous surface vehicles to achieve practical prescribed-time cooperative path following without velocity measurements, effectively managing unmodeled dynamics and actuator saturation. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to address the challenges of path following for underactuated ASVs without velocity measurements and in the presence of actuator saturation.

February 22, 2026

Practical Prescribed-Time Cooperative Path Following of Underactuated Multi-ASVs Without Velocity Measurements via Intermittent Control

Key Points

The study aims to address the challenges of path following for underactuated ASVs without velocity measurements and in the presence of actuator saturation.
Developed a practical prescribed-time velocity observer to estimate unmeasurable velocity information.
Designed a cooperative guidance law for synchronized path following using aperiodic intermittent communication.
Created an intermittent controller incorporating neural networks to handle unmodeled dynamics and actuator saturation.
Constructed an adaptive law for estimating optimal neural network weights.
The closed-loop system converges to a residual set within the prescribed-time interval.
Numerical simulations demonstrate the effectiveness of the proposed path following algorithms.

Abstract

In this article, the problem of practical prescribed-time (PT) cooperative path following (CPF) is investigated for underactuated autonomous surface vehicles (ASVs), which are not equipped with velocity sensors and subject to unmodeled dynamics and actuator saturation. First, a practical PT velocity observer (PTVO) is designed to estimate unmeasurable velocity information, which is then employed in the design of the guidance law and controller. At the kinematic level, a cooperative guidance law based on aperiodic intermittent communication is developed for synchronized path following, effectively saving communication resources. At the dynamic level, an aperiodic intermittent controller incorporating neural networks (NNs) is designed to approximate unmodeled dynamics and effectively avoid continuous operation of actuators with input saturation. Meanwhile, the intermittent adaptive law is constructed to estimate the optimal weights of the NNs, thereby reducing their complexity. The closed-loop system is verified to converge to a residual set within a PT interval. Finally, we conduct numerical simulations to demonstrate the effectiveness of the proposed algorithms.

AIに質問

Bookmark