An Observer-Based Adaptive Neural Network Finite-Time Tracking Control for Autonomous Underwater Vehicles via Command Filters

IF 4.4 2区 地球科学 Q1 REMOTE SENSING
Drones Pub Date : 2023-09-26 DOI:10.3390/drones7100604
Jun Guo, Jun Wang, Yuming Bo
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引用次数: 1

Abstract

Due to the hostile marine environment, there will inevitably be unpredictable factors during the operation of unmanned underwater vehicles, including changes in ocean currents, hull dimensions, and velocity measurement uncertainties. An improved finite-time adaptive tracking control issue is considered for autonomous underwater vehicles (AUVs) with uncertain dynamics, unknown external disturbances, and unavailable speed information. A state observer is designed to estimate the position and velocity of the vehicle via a neural network (NN) approach. The NN is used to estimate uncertainties and external disturbances. A finite-time controller is designed via backstepping and command filter techniques. A multi-input multi-output (MIMO) filter for AUVs is established, and the corresponding MIMO filter compensation signal is constructed to eliminate the effect of filtering error. All the signals of the closed-loop system are proved to be finite-time bounded. An example with comparison is given to show the effectiveness of our method.
基于观测器的自适应神经网络指令滤波器自主水下航行器有限时间跟踪控制
由于恶劣的海洋环境,无人潜航器在作业过程中不可避免地会出现不可预测的因素,包括洋流的变化、船体尺寸的变化、测速的不确定性等。针对动态不确定、外部干扰未知、速度信息不可用的自主水下航行器,研究了一种改进的有限时间自适应跟踪控制问题。设计了一个状态观测器,通过神经网络方法估计车辆的位置和速度。神经网络用于估计不确定性和外部干扰。通过反步和命令滤波技术设计了有限时间控制器。建立了水下机器人多输入多输出(MIMO)滤波器,构造了相应的MIMO滤波器补偿信号,消除了滤波误差的影响。证明了闭环系统的所有信号都是有限时间有界的。最后通过一个算例对比说明了该方法的有效性。
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
Drones
Drones Engineering-Aerospace Engineering
CiteScore
5.60
自引率
18.80%
发文量
331
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