Adaptive path following control for autonomous surface vehicles towed by a single tugboat with output constraints

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Qiang Guo, Xianku Zhang
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引用次数: 0

Abstract

This paper proposes an adaptive fixed-time path-following control scheme for autonomous tugboats towing vessels, addressing both performance and feasibility constraints. To establish a clear path relationship, the kinematic models of the tugboat and the towed vessel are decoupled, allowing coordinated control through the tugboat’s propulsion system and towline dynamics. Building on this foundation, a barrier Lyapunov function combined with an adaptive backstepping approach is employed to design a controller that ensures high navigation accuracy while meeting output constraints. To achieve seamless coordination, a consensus-based dynamic controller is developed, enabling velocity and heading synchronization between the tugboat and the towed vessel. Furthermore, a fixed-time convergence algorithm is incorporated, guaranteeing system stability and error convergence within a predefined time frame. To enhance robustness against nonlinear uncertainties and external disturbances, radial basis neural networks and adaptive robust terms are integrated into the control framework. Finally, simulation results validate the effectiveness of the proposed approach, demonstrating precise path-following performance and reliable error convergence under complex operational conditions.
带输出约束的单拖船拖曳自主水面车辆自适应路径跟踪控制。
本文提出了一种自动拖船拖曳船舶的自适应固定时间路径跟踪控制方案,解决了拖船拖曳船舶的性能和可行性约束。为了建立清晰的路径关系,拖船和被拖船的运动学模型被解耦,通过拖船的推进系统和拖线动力学进行协调控制。在此基础上,采用barrier Lyapunov函数结合自适应反演方法设计了在满足输出约束的同时保证高导航精度的控制器。为了实现无缝协调,开发了一种基于共识的动态控制器,使拖船和被拖船之间的速度和航向同步。在此基础上,引入了一种固定时间收敛算法,保证了系统在预定时间内的稳定性和误差收敛性。为了增强对非线性不确定性和外部干扰的鲁棒性,在控制框架中集成了径向基神经网络和自适应鲁棒项。最后,仿真结果验证了该方法的有效性,在复杂操作条件下具有精确的路径跟踪性能和可靠的误差收敛性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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