This article investigates the problem of path following for the underactuated unmanned surface vehicles (USVs) subject to state constraints. A useful control algorithm is proposed by combining the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The presented approach consists of three modules: guidance law, dynamic controller, and event triggering. First, to deal with the “singularity” problem, the guidance-based path-following (GBPF) principle is introduced in the guidance law loop. In contrast to the traditional barrier Lyapunov function (BLF) method, this article converts the USV’s constraint model to a class of nonlinear systems without state constraints by introducing a nonlinear mapping. The control signal generated by the dynamic controller module consists of a backstepping-based feedforward control signal and an ADP-based approximate optimal feedback control signal. Therefore, the presented scheme can guarantee the approximate optimal performance. To approximate the cost function and its partial derivative, a critic neural network (NN) is constructed. By considering the event-triggered condition, the dynamic controller is further improved. Compared with traditional time-triggered control methods, the proposed approach can greatly reduce communication and computational burdens. This article proves that the closed-loop system is stable, and the simulation results and experimental validation are given to illustrate the effectiveness of the proposed approach.

中文翻译：

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具有状态约束的无人地面车辆的事件触发近似最优路径跟踪控制


本文研究了受状态约束的欠驱动无人水面车辆（USV）的路径跟踪问题。通过结合反步技术、自适应动态规划（ADP）和事件触发机制，提出了一种有用的控制算法。所提出的方法由三个模块组成：制导律、动态控制器和事件触发。首先，为了解决“奇点”问题，在制导律循环中引入了基于制导的路径跟踪（GBPF）原理。与传统的势垒李亚普诺夫函数（BLF）方法相比，本文通过引入非线性映射，将USV的约束模型转换为一类无状态约束的非线性系统。动态控制器模块产生的控制信号由基于反步的前馈控制信号和基于ADP的近似最优反馈控制信号组成。因此，所提出的方案可以保证近似最优的性能。为了近似成本函数及其偏导数，构建了批评神经网络（NN）。通过考虑事件触发条件，进一步改进了动态控制器。与传统的时间触发控制方法相比，所提出的方法可以大大减少通信和计算负担。本文证明了闭环系统是稳定的，并给出了仿真结果和实验验证，说明了该方法的有效性。