Predefined-Time Adaptive Neural Control with Event-Triggering for Robust Trajectory Tracking of Underactuated Marine Vessels

This paper addresses the trajectory tracking control problem of underactuated ships in ocean engineering, which faces the dual challenges of tracking error time–performance regulation and robustness design due to the system’s underactuated characteristics, model uncertainties, and external disturban...

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Bibliographic Details
Published in:Processes Vol. 13; no. 8; p. 2443
Main Authors: An, Hui, Yu, Zhanyang, Zhang, Jianhua, Wang, Xinxin, Chin, Cheng Siong
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2025
Subjects:
Accuracy
Adaptive control
Analysis
Approximation
Closed loops
Consumption (Economics)
Control systems
Convergence
Design
Designers
Disturbances
Dynamical systems
Feedback control
Liapunov functions
Marine environment
Neural networks
Nonlinear dynamics
Nonlinear systems
Ocean engineering
Radial basis function
Real time
Robust control
Sea vessels
Time dependence
Tracking control
Tracking errors
Unmanned vehicles
Windows (intervals)
ISSN:2227-9717, 2227-9717
Online Access:Get full text
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Summary:This paper addresses the trajectory tracking control problem of underactuated ships in ocean engineering, which faces the dual challenges of tracking error time–performance regulation and robustness design due to the system’s underactuated characteristics, model uncertainties, and external disturbances. Aiming to address the issues of traditional finite-time control (convergence time dependent on initial states) and fixed-time control (control chattering and parameter conservativeness), this paper proposes a predefined-time adaptive control framework that integrates an event-triggered mechanism and neural networks. By constructing a Lyapunov function with time-varying weights and designing non-periodic dynamically updated dual triggering conditions, the convergence process of tracking errors is strictly constrained within a user-prespecified time window without relying on initial states or introducing non-smooth terms. An adaptive approximator based on radial basis function neural networks (RBF-NNs) is employed to compensate for unknown nonlinear dynamics and external disturbances in real-time. Combined with the event-triggered mechanism, it dynamically adjusts the update instances of control inputs, ensuring prespecified tracking accuracy while significantly reducing computational resource consumption. Theoretical analysis shows that all signals in the closed-loop system are uniformly ultimately bounded, tracking errors converge to a neighborhood of the origin within the predefined-time, and the update frequency of control inputs exhibits a linear relationship with the predefined-time, avoiding Zeno behavior. Simulation results verify the effectiveness of the proposed method in complex marine environments. Compared with traditional control strategies, it achieves more accurate trajectory tracking, faster response, and a substantial reduction in control input update frequency, providing an efficient solution for the engineering implementation of embedded control systems in unmanned ships.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr13082443