{"title":"Dynamic-Memory Event-Triggered-Based Optimal 3D Path-Following Control for AUV Systems Under Sparse Sensor Attacks","authors":"Ziyi Qiu, Yingnan Pan, Zhechen Zhu, Yan Lei","doi":"10.1002/rnc.7980","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This paper designs an optimal three-dimensional path-following control scheme for the underactuated autonomous underwater vehicle (AUV) incorporating a novel dynamic memory event-triggered mechanism (DMETM) against sparse sensor attacks. To address system state measurement errors and disturbances caused by sparse sensor attacks leading to a decrease in control effectiveness, an improved estimation algorithm is proposed, which can estimate the outputs of sensors under attacks and reduce the impact of disturbances. In order to reduce the impact of unqualified data on AUV system performance, based on the improved estimation algorithm, additional judging conditions are introduced in DMETM ensuring the accuracy of triggers. The results of stability analysis show all signals in the AUV system are bounded. Finally, some simulation results display the effectiveness of the proposed control scheme.</p>\n </div>","PeriodicalId":50291,"journal":{"name":"International Journal of Robust and Nonlinear Control","volume":"35 12","pages":"5260-5273"},"PeriodicalIF":3.2000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Robust and Nonlinear Control","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rnc.7980","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper designs an optimal three-dimensional path-following control scheme for the underactuated autonomous underwater vehicle (AUV) incorporating a novel dynamic memory event-triggered mechanism (DMETM) against sparse sensor attacks. To address system state measurement errors and disturbances caused by sparse sensor attacks leading to a decrease in control effectiveness, an improved estimation algorithm is proposed, which can estimate the outputs of sensors under attacks and reduce the impact of disturbances. In order to reduce the impact of unqualified data on AUV system performance, based on the improved estimation algorithm, additional judging conditions are introduced in DMETM ensuring the accuracy of triggers. The results of stability analysis show all signals in the AUV system are bounded. Finally, some simulation results display the effectiveness of the proposed control scheme.
期刊介绍：
Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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