具有扰动观测器的自主潜水器全预定性能轨迹跟踪控制策略

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

摘要

本文研究了自主潜水器(AUV)的轨迹跟踪控制,其一般不确定性包括模型不确定性和未知洋流干扰。本文提出了一种基于扰动观测器的全规定性能控制策略,确保跟踪误差、速度误差和观测误差都受到约束。首先,在 AUV 加速度不可测量的情况下,构建一个固定时间观测器来估计一般不确定性,通过规定性能观测器将观测误差限制在规定精度内。然后,根据性能函数和相应的误差变换,设计一个规定性能协议来实现轨迹跟踪控制,从而将观测误差、跟踪误差和速度误差都限制在规定精度范围内。仿真结果表明了全规定性能控制策略的效率,同时全状态约束下的 AUV 跟踪控制是可行的。此外,与其他两篇相关研究相比,本研究在深水干扰和近水面干扰情况下都将观测性能提高了至少 10%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Full prescribed performance trajectory tracking control strategy of autonomous underwater vehicle with disturbance observer

This paper investigates trajectory tracking control of the Autonomous Underwater Vehicle (AUV) with the general uncertainty consisting of model uncertainties and unknown ocean current disturbances. A full prescribed performance control strategy based on disturbance observer is developed, which ensures that the tracking error, the velocity error, and the observation error are all constrained. First, under the case of unmeasurable AUV acceleration, a fixed-time observer is constructed to estimate the general uncertainty, which constrains the observation error within the prescribed accuracy by a prescribed performance observer. Then, based on the performance function and corresponding error transformation, a prescribed performance protocol is designed to realize the trajectory tracking control, so that the observation error, the tracking error, and the velocity error are all constrained within the prescribed accuracy range. Simulation results demonstrate the efficiency of the full prescribed performance control strategy while the AUV tracking control with full state constraints is feasible. Moreover, compared with the other two relevant works, this study improves the observation performance by at least 10 %, both in case of deepwater disturbances and near-surface disturbances.

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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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