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不确定非完整系统的鲁棒输出反馈镇定与跟踪及其在移动机器人中的应用

Robust Output Feedback Stabilization and Tracking for an Uncertain Nonholonomic Systems with Application to a Mobile Robot.

作者信息

Rabbani Muhammad Junaid, Memon Attaullah Y, Farhan Muhammad, Larik Raja Masood, Ashraf Shahzad, Burhan Khan Muhammad, Arfeen Zeeshan Ahmad

机构信息

Department of Electrical Engineering, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan.

Department of Electronics and Power Engineering, PN Engineering College, National University of Sciences and Technology, Karachi 75500, Pakistan.

出版信息

Sensors (Basel). 2024 Jun 3;24(11):3616. doi: 10.3390/s24113616.

DOI:10.3390/s24113616
PMID:38894407
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11175306/
Abstract

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input-output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties.

摘要

本文提出了一种新颖的鲁棒输出反馈控制方法,该方法能够同时对一类欠驱动非完整系统进行镇定和轨迹跟踪,尽管存在模型不确定性、外部干扰且无速度测量。为解决这一具有挑战性的问题,通过采用输入-输出反馈线性化方法和坐标变换(微分同胚)成功创建了一种广义范式。本研究主要关注可转化为范式的非完整系统的镇定问题,该问题存在若干挑战,包括:(i)非三角范式;(ii)系统的内部动态在控制中是非仿射的;(iii)系统的零动态不是最小相位的。所提出的方案利用了组合反步和滑模控制(SMC)技术。此外,还开发了全阶高增益观测器(HGO)来估计输出函数的导数和内部动态。然后,将全阶HGO和反步SMC相结合以合成一个鲁棒输出反馈控制器。以差动驱动型(2,0)轮式移动机器人为例来支持理论结果。仿真结果表明,反步SMC对有界不确定性具有鲁棒性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/11a7d68a113f/sensors-24-03616-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/28c0f3bc2b63/sensors-24-03616-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/19ff3aa42e74/sensors-24-03616-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/9265f81e333a/sensors-24-03616-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/f61dc7e9b6a2/sensors-24-03616-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/93739051dfcd/sensors-24-03616-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/11a7d68a113f/sensors-24-03616-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/28c0f3bc2b63/sensors-24-03616-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/19ff3aa42e74/sensors-24-03616-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/9265f81e333a/sensors-24-03616-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/f61dc7e9b6a2/sensors-24-03616-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/93739051dfcd/sensors-24-03616-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73c0/11175306/11a7d68a113f/sensors-24-03616-g006.jpg

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本文引用的文献

1
Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.从单轮式移动机器人的自适应神经网络输出反馈控制中学习。
ISA Trans. 2016 Mar;61:337-347. doi: 10.1016/j.isatra.2016.01.005. Epub 2016 Jan 29.