Analysis reveals a novel control approach improving convergence in unmanned surface vehicles, suggesting enhanced tracking of non-cooperative targets.
This paper investigates the target-guided coordinated control (TACC) of unmanned surface vehicles (USVs). To enable USVs to rapidly track non-cooperative targets, a prescribed-time TACC method based on novel Chebyshev orthogonal neural network (CONN) is proposed in this paper. Firstly, a novel TACC architecture with decoupled kinematic and kinetic subsystems is designed, including dynamic surface control technology for tracking the non-cooperative target accurately. Secondly, a prescribed-time TACC system is proposed to make system states achieve convergence within the prescribed time. Finally, the prescribed-time theory is introduced into the weight update law of CONN, which enables more accurate and faster estimation of external time-varying disturbance. The effectiveness of the convergence time through the overall TACC system is verified through stability analysis. The comparative simulation results indicate the effectiveness of the proposed control method.
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Li et al. (2025) studied this question.
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