A novel fixed-time zeroing neural network and its application to path tracking control of wheeled mobile robots

Peng Miao; Daoyuan Zhang; Shuai Li

doi:10.1016/j.cam.2024.116402

A novel fixed-time zeroing neural network and its application to path tracking control of wheeled mobile robots

Peng Miao^*, Daoyuan Zhang, Shuai Li

^*Corresponding author for this work

BA56 Cognitive production industry

Research output: Contribution to journal › Article › Scientific › peer-review

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.

Original language	English
Article number	116402
Journal	Journal of Computational and Applied Mathematics
Volume	460
DOIs	https://doi.org/10.1016/j.cam.2024.116402
Publication status	Published - 1 May 2025
MoE publication type	A1 Journal article-refereed

Funding

This work was supported by the Key Scientific Research Foundation of Education Bureau of Henan Province, China (Grant No. 25B110021), the Henan Provincial Science and Technology Research Project (No. 222102320404), the Guangdong Provincial Key Construction Discipline Research Ability Enhancement Project (2022ZDJS152), the Guangdong Provincial College Mathematics Teaching Steering Committee (GDSXJ G202326) and the Research Foundation of Guangzhou Xinhua University (2019KYQN14).

Keywords

FDTS
Path tracking
UBT
Wheeled mobile robot
Zeroing neural network

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10.1016/j.cam.2024.116402

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title = "A novel fixed-time zeroing neural network and its application to path tracking control of wheeled mobile robots",

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.",

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AU - Miao, Peng

AU - Zhang, Daoyuan

AU - Li, Shuai

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Y1 - 2025/5/1

N2 - 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.

AB - 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.

KW - FDTS

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KW - UBT

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KW - Zeroing neural network

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A novel fixed-time zeroing neural network and its application to path tracking control of wheeled mobile robots

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