基于混合控制算法的爬壁机器人跟踪控制

TP242; 为解决爬壁机器人轨迹跟踪过程中速度突变与输入抖振的问题,提出一种基于神经动力学模型的反演运动学控制器与组合趋近律神经滑模动力学控制的混合鲁棒控制算法.利用神经动力学模型获取有界、平滑的虚拟位姿误差信号,解决了传统反演控制法引起的速度跳变问题;引入自适应径向基神经网络(RBFNN)调节基于组合趋近律的滑模增益,消除了抖振现象.设计过程采用Lyapunov函数,保证了控制系统的稳定与收敛.通过仿真数据与实验结果证明了所提算法的有效性....

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Published in计算机集成制造系统 Vol. 29; no. 11; pp. 3560 - 3571
Main Authors 张小俊, 吴亚淇, 谢必成, 赵金亮
Format Journal Article
LanguageChinese
Published 河北工业大学 机械工程学院,天津 300130 01.11.2023
Subjects
神经动力学模型
neural sliding mode control
hybrid control
轨迹跟踪
反演控制
混合控制
neuro dynamics model
wall climbing robot
神经滑模控制
爬壁机器人
trajectory tracking
backstepping control
Online AccessGet full text
ISSN1006-5911
DOI10.13196/j.cims.2022.0895

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Abstract TP242; 为解决爬壁机器人轨迹跟踪过程中速度突变与输入抖振的问题,提出一种基于神经动力学模型的反演运动学控制器与组合趋近律神经滑模动力学控制的混合鲁棒控制算法.利用神经动力学模型获取有界、平滑的虚拟位姿误差信号,解决了传统反演控制法引起的速度跳变问题;引入自适应径向基神经网络(RBFNN)调节基于组合趋近律的滑模增益,消除了抖振现象.设计过程采用Lyapunov函数,保证了控制系统的稳定与收敛.通过仿真数据与实验结果证明了所提算法的有效性.
AbstractList TP242; 为解决爬壁机器人轨迹跟踪过程中速度突变与输入抖振的问题,提出一种基于神经动力学模型的反演运动学控制器与组合趋近律神经滑模动力学控制的混合鲁棒控制算法.利用神经动力学模型获取有界、平滑的虚拟位姿误差信号,解决了传统反演控制法引起的速度跳变问题;引入自适应径向基神经网络(RBFNN)调节基于组合趋近律的滑模增益,消除了抖振现象.设计过程采用Lyapunov函数,保证了控制系统的稳定与收敛.通过仿真数据与实验结果证明了所提算法的有效性.
Author 张小俊
赵金亮
吴亚淇
谢必成
AuthorAffiliation 河北工业大学 机械工程学院,天津 300130
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XIE Bicheng
ZHANG Xiaojun
ZHAO Jinliang
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DocumentTitle_FL Tracking control of wall climbing robot based on hybrid control algorithm
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Keywords 神经动力学模型
neural sliding mode control
hybrid control
轨迹跟踪
反演控制
混合控制
neuro dynamics model
wall climbing robot
神经滑模控制
爬壁机器人
trajectory tracking
backstepping control
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Title 基于混合控制算法的爬壁机器人跟踪控制
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