文献类型：期刊文献

英文题名：Event-Triggered Approximate Optimal Path-Following Control for Unmanned Surface Vehicles With State Constraints

作者：Zhou, Weixiang[1];Fu, Jun[2];Yan, Huaicheng[3];Du, Xin[4];Wang, Yueying[4];Zhou, Hua[4]

机构：[1]Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China;[2]Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China;[3]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[4]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China

年份：2023

卷号：34

期号：1

起止页码：104

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20213210745665);WOS:【SCI-EXPANDED(收录号:WOS:000732191000001)】；

基金：This work was supported by the National Natural Science Foundation of China under Grant 61973204.This work was supported by the National Natural Science Foundation of China under Grant

语种：英文

外文关键词：Backstepping; Artificial neural networks; Vehicle dynamics; Stability analysis; Uncertainty; Sea surface; Learning systems; Adaptive dynamic programming (ADP); backstepping control; event-triggered control; neural network (NN); path following; state constraints; unmanned surface vehicle (USV)

摘要：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.