水下无人系统学报2024，Vol.32Issue(2)：376-382,7.DOI:10.11993/j.issn.2096-3920.2023-0041
输入饱和下AUV自适应神经网络预设性能控制
Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation
摘要
Abstract
In view of system uncertainty and input saturation of autonomous undersea vehicles(AUVs),an improved adaptive neural network-based prescribed performance control strategy was proposed to track the desired trajectory.Firstly,the nonlinear transformation was introduced to ensure that the position error remained within the preset time-varying range,improving control accuracy.Based on backstepping and Lyapunov functions,a virtual control law for the system was designed.Then,the neural network technology was used to process the unknown parameters of the system model,and the real control law of the system was reconstructed,which simplified the traditional backstepping control strategy and effectively reduced the computational complexity.Then,based on the Lyapunov stability theory,all the error signals of the AUV system were confirmed to be bounded.Finally,compared with traditional dynamic surface control methods,the simulation results show that the proposed control strategy has better control performance and can effectively overcome the impact of uncertainty on system performance by considering input saturation,effectively tracking target trajectories.关键词
自主水下航行器/神经网络/反步控制/轨迹跟踪Key words
autonomous undersea vehicle/neural network/backstepping control/trajectory tracking分类
军事科技引用本文复制引用
徐文峰,刘加朋,于金鹏,韩亚宁..输入饱和下AUV自适应神经网络预设性能控制[J].水下无人系统学报,2024,32(2):376-382,7.基金项目
山东省自然科学基金资助项目(ZR2020QF063). （ZR2020QF063）
