一类无人艇的重复学习复合抗扰容错控制  

Repetitive learning composite anti-disturbance fault-tolerant control for unmanned marine vehicles

作　　者：张健 朱延正 苏春翌[2] 童显芳 ZHANG Jian;ZHU Yan-zheng;SU Chun-yi;TONG Xian-fang(College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China;School of Engineering and Computer Science,Concordia University,Montreal QC H3G 1M8,Canada)

机构地区：[1]山东科技大学电气与自动化工程学院,山东青岛266590 [2]康考迪亚大学工程与计算机科学学院,加拿大蒙特利尔QC H3G 1M8

出　　处：《控制与决策》2025年第1期261-270,共10页Control and Decision

基　　金：国家自然科学基金项目(62222310,61973131,62033008);山东省泰山学者青年专家项目。

摘　　要：针对一类具有多重扰动、不可测速度信息和执行器故障的无人艇系统,提出一种重复学习复合抗扰容错控制策略.首先,为了克服速度信息不可用的不足,提出一种新的集成故障-扰动估计器,实现故障与干扰之和的准确估计;然后,基于观测信息,通过设计一类比例微分型重复学习复合抗扰容错控制器来实现周期性轨迹跟踪,相比于传统比例微分型重复学习控制器,所提出基于观测器的重复学习控制器不需要利用测量输出的导数且具有更大的控制自由度;最后,通过对一类无人艇动力定位系统的仿真,验证所提出重复学习复合抗扰容错控制策略的有效性和优势.This paper investigates the repetitive learning composite anti-disturbance fault-tolerant(CADFT)control issue for a class of unmanned marine vehicles(UMVs)with unmeasured velocity information,actuator faults and multiple disturbances.To overcome the absence of velocity information,a new integrated-fault-disturbance estimator is proposed to achieve the accurate estimation of the sum of faults and disturbances.Based on the observed information,a new kind of observer-based proportional-derivative(PD)-type repetitive learning CADFT controller is developed to realise the periodic trajectory tracking.The proposed repetitive learning controller avoids using the time derivative of measured outputs and provides more freedom degree of control than the traditional PD-type repetitive learning controller.Finally,a numerical simulation on UMVs in the dynamic positioning mode is carried out to show the effectiveness and superiority of the proposed repetitive learning CADFT control method.

关 键 词：无人艇 复合抗扰容错控制 重复学习控制 执行器故障 多重扰动 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]