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基于有限时间收敛归零神经网络的履带式移动机器人鲁棒控制

Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network.

作者信息

Cao Yuxuan, Liu Boyun, Pu Jinyun

机构信息

College of Power Engineering, Naval University of Engineering, Wuhan, China.

出版信息

Front Neurorobot. 2023 Sep 20;17:1242063. doi: 10.3389/fnbot.2023.1242063. eCollection 2023.

DOI:10.3389/fnbot.2023.1242063
PMID:37799573
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10547886/
Abstract

INTRODUCTION

Since tracked mobile robot is a typical non-linear system, it has been a challenge to achieve the trajectory tracking of tracked mobile robots. A zeroing neural network is employed to control a tracked mobile robot to track the desired trajectory.

METHODS

A new fractional exponential activation function is designed in this study, and the implicit derivative dynamic model of the tracked mobile robot is presented, termed finite-time convergence zeroing neural network. The proposed model is analyzed based on the Lyapunov stability theory, and the upper bound of the convergence time is given. In addition, the robustness of the finite-time convergence zeroing neural network model is investigated under different error disturbances.

RESULTS AND DISCUSSION

Numerical experiments of tracking an eight-shaped trajectory are conducted successfully, validating the proposed model for the trajectory tracking problem of tracked mobile robots. Comparative results validate the effectiveness and superiority of the proposed model for the kinematical resolution of tracked mobile robots even in a disturbance environment.

摘要

引言

由于履带式移动机器人是典型的非线性系统，实现履带式移动机器人的轨迹跟踪一直是一项挑战。采用归零神经网络来控制履带式移动机器人跟踪期望轨迹。

方法

本研究设计了一种新的分数指数激活函数，提出了履带式移动机器人的隐式导数动态模型，称为有限时间收敛归零神经网络。基于李雅普诺夫稳定性理论对所提出的模型进行了分析，并给出了收敛时间的上界。此外，研究了有限时间收敛归零神经网络模型在不同误差干扰下的鲁棒性。

结果与讨论

成功进行了跟踪八字形轨迹的数值实验，验证了所提出的模型对于履带式移动机器人轨迹跟踪问题的有效性。对比结果验证了所提出的模型即使在干扰环境下对于履带式移动机器人运动学求解的有效性和优越性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/869b639920c3/fnbot-17-1242063-g0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/fd450ee73268/fnbot-17-1242063-g0001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/d1c7cc28700a/fnbot-17-1242063-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/a4ce18121f12/fnbot-17-1242063-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/4245e7e48b99/fnbot-17-1242063-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/963bdd4eefa6/fnbot-17-1242063-g0008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/869b639920c3/fnbot-17-1242063-g0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/fd450ee73268/fnbot-17-1242063-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/02b377f76cba/fnbot-17-1242063-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/1f6cc7b46f1c/fnbot-17-1242063-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/6a6f191748b4/fnbot-17-1242063-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/d1c7cc28700a/fnbot-17-1242063-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/a4ce18121f12/fnbot-17-1242063-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/4245e7e48b99/fnbot-17-1242063-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/963bdd4eefa6/fnbot-17-1242063-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/c267df021783/fnbot-17-1242063-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/051a842d5f53/fnbot-17-1242063-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/9a17f022ab75/fnbot-17-1242063-g0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/971a/10547886/869b639920c3/fnbot-17-1242063-g0012.jpg

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本文引用的文献

1
A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators.一种应用于移动机器人操纵器的新型超扭曲归零神经网络。
IEEE Trans Neural Netw Learn Syst. 2021 Apr;32(4):1776-1787. doi: 10.1109/TNNLS.2020.2991088. Epub 2021 Apr 2.
2
Robust Zeroing Neural-Dynamics and Its Time-Varying Disturbances Suppression Model Applied to Mobile Robot Manipulators.应用于移动机器人操纵器的鲁棒归零神经动力学及其时变干扰抑制模型
IEEE Trans Neural Netw Learn Syst. 2018 Sep;29(9):4385-4397. doi: 10.1109/TNNLS.2017.2764529. Epub 2017 Nov 9.
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A recurrent neural network for solving Sylvester equation with time-varying coefficients.
一种用于求解具有时变系数的西尔维斯特方程的递归神经网络。
IEEE Trans Neural Netw. 2002;13(5):1053-63. doi: 10.1109/TNN.2002.1031938.