{"title":"A novel fixed-time zeroing neural network and its application to path tracking control of wheeled mobile robots","authors":"Peng Miao , Daoyuan Zhang , Shuai Li","doi":"10.1016/j.cam.2024.116402","DOIUrl":null,"url":null,"abstract":"<div><div>Based on the current fixed-time stability criteria, a new Lyapunov function is designed to achieve fixed-time stability for the nonlinear dynamical system. It contains an exponential function term which can make the convergence rate faster. This paper gives the proof of our fixed-time stability criterion and estimates the upper bound of convergence time. The upper bound of convergence time is relatively smaller because it is a constant compounded by a two-layer logarithmic function. While, the impact of parameters is analyzed and some strategies for parameter selection are provided. On the basis of this achievement, we give a novel fixed-time zeroing neural network and it is applied into the wheeled mobile robot path tracking problem. Lastly, simulation results show the validity of our methods.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"460 ","pages":"Article 116402"},"PeriodicalIF":2.1000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational and Applied Mathematics","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0377042724006502","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
Based on the current fixed-time stability criteria, a new Lyapunov function is designed to achieve fixed-time stability for the nonlinear dynamical system. It contains an exponential function term which can make the convergence rate faster. This paper gives the proof of our fixed-time stability criterion and estimates the upper bound of convergence time. The upper bound of convergence time is relatively smaller because it is a constant compounded by a two-layer logarithmic function. While, the impact of parameters is analyzed and some strategies for parameter selection are provided. On the basis of this achievement, we give a novel fixed-time zeroing neural network and it is applied into the wheeled mobile robot path tracking problem. Lastly, simulation results show the validity of our methods.
期刊介绍：
The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest.
The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
