{"title":"Observer-based self-triggered adaptive neural network control for nonlinear systems with prescribed time","authors":"","doi":"10.1016/j.jfranklin.2024.107241","DOIUrl":null,"url":null,"abstract":"<div><p>This paper is concerned with the adaptive neural networks (NNs) prescribed-time control problem for a class of strict-feedback nonlinear systems subject to unmeasured states via the self-triggered control (STC). By developing a new state observer with prescribed-time function, an adaptive NNs self-triggered controller is designed to solve the problem of prescribed-time performance (PTP). Due to the initiative of the STC, it has excellent practical significance in terms of contracting computing resources and network communication resources. With the proposed new strategy, the PTP of the closed-loop system can be guaranteed, and all the signals within the closed-loop system are bounded. Finally, the practicability and effectiveness of the above prescribed-time STC algorithm are verified via some physical simulations.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Franklin Institute-engineering and Applied Mathematics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0016003224006628","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 is concerned with the adaptive neural networks (NNs) prescribed-time control problem for a class of strict-feedback nonlinear systems subject to unmeasured states via the self-triggered control (STC). By developing a new state observer with prescribed-time function, an adaptive NNs self-triggered controller is designed to solve the problem of prescribed-time performance (PTP). Due to the initiative of the STC, it has excellent practical significance in terms of contracting computing resources and network communication resources. With the proposed new strategy, the PTP of the closed-loop system can be guaranteed, and all the signals within the closed-loop system are bounded. Finally, the practicability and effectiveness of the above prescribed-time STC algorithm are verified via some physical simulations.
期刊介绍:
The Journal of The Franklin Institute has an established reputation for publishing high-quality papers in the field of engineering and applied mathematics. Its current focus is on control systems, complex networks and dynamic systems, signal processing and communications and their applications. All submitted papers are peer-reviewed. The Journal will publish original research papers and research review papers of substance. Papers and special focus issues are judged upon possible lasting value, which has been and continues to be the strength of the Journal of The Franklin Institute.