State observer-based Physics-Informed Machine Learning for leader-following tracking control of mobile robot

被引：0
作者：
Park, Sejun [1 ]
Lee, S. M. [1 ]
机构：
[1] Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea
来源：
ISA TRANSACTIONS | 2024年 / 146卷
基金：
新加坡国家研究基金会;
关键词：
Physics-Informed Machine Learning (PIML); State observer; Time-varying parameter estimation; System identification; Leader-following tracking control;
D O I：
10.1016/j.isatra.2024.01.001
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the novel leader -following tracking control method is proposed for mobile robots, which consists estimation technique of the speed of the leader robot (LR), and a parameter -dependent controller for the follower robot (FR). To estimate the speed of LR, a novel Physics Informed Machine Learning (PIML) is proposed to learn the dynamics of the state observer via the error state model. The dynamics of the state observer in PIML play a significant role for stable learning and state estimation of uncertain models. The gain of the parameter -dependent controller is determined by the convex combination of the robust control technique via the polytopic model. Finally, the tracking performance of the proposed method is verified through the simulation and experiment.
引用
收藏
页码：582 / 591
页数：10
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