{"title":"Adaptive Neural Network Finite-Time Event Triggered Intelligent Control for Stochastic Nonlinear Systems With Time-Varying Constraints","authors":"Jia Liu;Jiapeng Liu;Qing-Guo Wang;Jinpeng Yu","doi":"10.1109/TAI.2024.3497913","DOIUrl":null,"url":null,"abstract":"Finite-time command-filter event-trigger control based on adaptive neural network is presented in this article for a class of output-feedback stochastic nonlinear system (SNS) with output time-varying constraints and unmeasured states. The adaptive neural network combined with backstepping is utilized to approximate the unknown nonlinear functions of the system. The finite-time command-filter is employed to reduce the difficulty of complex calculation caused by backstepping technique. An adaptive observer is developed to estimate unmeasured states, and a controller is designed to be triggered only when the event-triggered condition is met. The time-varying barrier Lyapunov function is utilized to ensure the output time-varying constraint. The control method proposed in this article not only guarantees the finite-time stability of the system but also meets the output constraint. The effectiveness of the method is demonstrated on the ship maneuvering system with three degrees of freedom.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"773-779"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on artificial intelligence","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10752919/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Finite-time command-filter event-trigger control based on adaptive neural network is presented in this article for a class of output-feedback stochastic nonlinear system (SNS) with output time-varying constraints and unmeasured states. The adaptive neural network combined with backstepping is utilized to approximate the unknown nonlinear functions of the system. The finite-time command-filter is employed to reduce the difficulty of complex calculation caused by backstepping technique. An adaptive observer is developed to estimate unmeasured states, and a controller is designed to be triggered only when the event-triggered condition is met. The time-varying barrier Lyapunov function is utilized to ensure the output time-varying constraint. The control method proposed in this article not only guarantees the finite-time stability of the system but also meets the output constraint. The effectiveness of the method is demonstrated on the ship maneuvering system with three degrees of freedom.