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基于 L1 反步自适应控制策略的先进自主水下航行器姿态控制

Advanced Autonomous Underwater Vehicles Attitude Control with L 1 Backstepping Adaptive Control Strategy.

机构信息

Department of Mechanical Engineering, University of Connecticut, Storrs CT 06269, USA.

出版信息

Sensors (Basel). 2019 Nov 7;19(22):4848. doi: 10.3390/s19224848.

DOI:10.3390/s19224848
PMID:31703300
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6891452/
Abstract

This paper presents a novel attitude control design, which combines L 1 adaptive control and backstepping control together, for Autonomous Underwater Vehicles (AUVs) in a highly dynamic and uncertain environment. The Euler angle representation is adopted in this paper to represent the attitude propagation. Kinematics and dynamics of the attitude are in the strict feedback form, which leads the backstepping control strategy serving as the baseline controller. Moreover, by bringing fast and robust adaptation into the backstepping control architecture, our controller is capable of dealing with time-varying uncertainties from modeling and external disturbances in dynamics. This attitude controller is proposed for coupled pitch-yaw channels. For inevitable roll excursions, a Lyapunov function-based optimum linearization method is presented to analyze the stability of the roll angle in the operation region. Theoretical analysis and simulation results are given to demonstrate the feasibility of the developed control strategy.

摘要

本文提出了一种新颖的姿态控制设计,将 L1 自适应控制与反步控制相结合,应用于高度动态和不确定环境中的自治水下机器人(AUV)。本文采用欧拉角表示来表示姿态传播。姿态的运动学和动力学采用严格反馈形式,这使得反步控制策略成为基准控制器。此外,通过将快速和鲁棒自适应引入反步控制架构,我们的控制器能够处理建模时的时变不确定性和动力学中的外部干扰。该姿态控制器针对耦合的俯仰-偏航通道提出。对于不可避免的滚转偏移,提出了一种基于李雅普诺夫函数的最优线性化方法来分析工作区域内滚转角的稳定性。给出了理论分析和仿真结果,以验证所提出控制策略的可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/7add21b00d64/sensors-19-04848-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/edcb9f3e1579/sensors-19-04848-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/66cf86eec24a/sensors-19-04848-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/b28c79b6fd04/sensors-19-04848-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/79f4662f60b2/sensors-19-04848-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/7c43862b3b3f/sensors-19-04848-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/9786edd7568e/sensors-19-04848-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/3986cd3a07d2/sensors-19-04848-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/7add21b00d64/sensors-19-04848-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/edcb9f3e1579/sensors-19-04848-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/66cf86eec24a/sensors-19-04848-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/b28c79b6fd04/sensors-19-04848-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/79f4662f60b2/sensors-19-04848-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/7c43862b3b3f/sensors-19-04848-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/9786edd7568e/sensors-19-04848-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/3986cd3a07d2/sensors-19-04848-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f3/6891452/7add21b00d64/sensors-19-04848-g007.jpg

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本文引用的文献

1
Novel L1 neural network adaptive control architecture with guaranteed transient performance.具有保证暂态性能的新型L1神经网络自适应控制架构。
IEEE Trans Neural Netw. 2007 Jul;18(4):1160-71. doi: 10.1109/TNN.2007.899197.