基于干扰观测器的AUV深度自适应终端滑模控制

doi:10.13543/j.bhxbzr.2021.01.014

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北京化工大学学报（自然科学版） ›› 2021, Vol. 48 ›› Issue (1) : 103-110. DOI: 10.13543/j.bhxbzr.2021.01.014

机电工程和信息科学

基于干扰观测器的AUV深度自适应终端滑模控制

饶志荣¹, 董绍江¹, 王军¹, 蔡巍巍², 刘伟¹

作者信息 +

Adaptive terminal sliding mode control of autonomous underwater vehicle (AUV) depth based on a disturbance observer

RAO ZhiRong¹, DONG ShaoJiang¹, WANG Jun¹, CAI WeiWei², LIU Wei¹

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文章历史 +

摘要

针对欠驱动自治水下机器人（autonomous underwater vehicle，AUV）在外部干扰和系统内部扰动下深度难以控制的问题，提出基于非线性干扰观测器（nonlinear disturbance observer，NDO）的自适应终端滑模控制方法。首先建立欠驱动AUV在垂直面上的状态方程并对其简化，其次根据简化后的系统状态方程构建NDO对外部干扰进行观测，再结合反步法设计出自适应终端滑模控制器；最后通过李雅普诺夫稳定性理论证明控制系统的稳定性。结果表明：欠驱动AUV最大跟踪误差为0.137 5 m，峰值时间为2.1 s，证明了所设计的控制器能够实现深度控制，降低抖振，具有较强的鲁棒性。

Abstract

The depth of an underactuated autonomous underwater vehicle (AUV) is difficult to control under external and internal disturbances. In an attempt to solve this problem, an adaptive terminal sliding control method based on a nonlinear disturbance observer (NDO) is proposed in this work. The state equation for the underactuated AUV in the vertical plane is first established and simplified. Secondly, based on the simplified state equation, the NDO to observe the external interference is constructed, and then the self-adaptive terminal sliding mode controller is designed by using a backstepping method. Finally, the stability of the control system is shown to be stable by using Lyapunov stability theory. The simulation results show that the maximum tracking error of the underactuated AUV is 0.137 5 m and the peak time is 2.1 s. These results show that the controller can achieve depth control with low chattering and strong robustness.

导出引用

饶志荣, 董绍江, 王军, 蔡巍巍, 刘伟. 基于干扰观测器的AUV深度自适应终端滑模控制[J]. 北京化工大学学报（自然科学版）, 2021, 48(1): 103-110 https://doi.org/10.13543/j.bhxbzr.2021.01.014

RAO ZhiRong, DONG ShaoJiang, WANG Jun, CAI WeiWei, LIU Wei. Adaptive terminal sliding mode control of autonomous underwater vehicle (AUV) depth based on a disturbance observer[J]. Journal of Beijing University of Chemical Technology, 2021, 48(1): 103-110 https://doi.org/10.13543/j.bhxbzr.2021.01.014

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基金

国家自然基金（51775072）；重庆市科技创新领军人才支持计划（CSTCCCXLJRC201920）；重庆交通大学研究生科研创新项目（20160108）

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收稿日期
2020-08-03
发布日期
2021-02-27

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