Zhu Guibing, Ma Yong, Yan Xinping
The School of Maritime, Zhejiang Ocean University, Zhoushan 316022, China.
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China.
Fundam Res. 2022 Oct 6;4(5):1254-1265. doi: 10.1016/j.fmre.2022.09.013. eCollection 2024 Sep.
This paper discusses the predefined practical finite-time (PPFT) dynamic positioning (DP) control problem for DP vessels subject to internal/external uncertainties. Those heterogeneity uncertainties are handled by a separate-type treatment approach. The finite-time (FT) DP control is fulfilled by a predefined FT function on the basis of a time-based generator (TBG). Under the dynamic surface control together with the TBG design framework, the convergence time and control accuracy of the DP system can be determined by the designer offline. Meanwhile, the virtual derivation and computational burden problems are dissolved by using a first-order filter and virtual parameter learning technique. To reduce mechanical wear, an event-triggering protocol between the control law and the actuator is built to reduce the operating frequency of the actuator. An event-triggered neuroadaptive PPFT control scheme is presented for DP vessels. The stability of the closed-loop DP control systems is validated via the Lyapunov theorem. Approach efficiency is confirmed by numerical examples.
本文讨论了受内部/外部不确定性影响的动力定位（DP）船舶的预定义实际有限时间（PPFT）动力定位控制问题。这些异质性不确定性通过一种分离式处理方法来处理。有限时间（FT）DP控制是基于基于时间的发生器（TBG）通过预定义的FT函数来实现的。在动态表面控制与TBG设计框架的共同作用下，DP系统的收敛时间和控制精度可以由设计者离线确定。同时，通过使用一阶滤波器和虚拟参数学习技术解决了虚拟导数和计算负担问题。为了减少机械磨损，在控制律和执行器之间建立了一种事件触发协议，以降低执行器的工作频率。提出了一种用于DP船舶的事件触发神经自适应PPFT控制方案。通过李雅普诺夫定理验证了闭环DP控制系统的稳定性。数值算例证实了方法的有效性。
